{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "be15e606",
   "metadata": {},
   "source": [
    "## 用MiniBatch-KMeans对生成的数据集进行聚类，并查看的效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "587b8c31",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "from sklearn.cluster import MiniBatchKMeans, KMeans\n",
    "from sklearn.metrics.pairwise import pairwise_distances_argmin\n",
    "from sklearn.datasets import make_blobs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d26a6dfd",
   "metadata": {},
   "source": [
    "## 设置属性防止中文乱码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1fb53970",
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['font.sans-serif'] = [u'SimHei']\n",
    "mpl.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5da3f22",
   "metadata": {},
   "source": [
    "## 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b0e4864",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000000\n",
      "<class 'numpy.ndarray'>\n",
      "1000000\n",
      "<class 'numpy.ndarray'>\n",
      "[[110.3665  20.0059]\n",
      " [110.3361  20.0364]\n",
      " [110.3333  20.0222]\n",
      " ...\n",
      " [110.3072  20.0114]\n",
      " [110.3318  19.9807]\n",
      " [110.2934  20.0233]]\n",
      "[20.0059 20.0364 20.0222 ... 20.0114 19.9807 20.0233]\n"
     ]
    }
   ],
   "source": [
    "X = []\n",
    "Y = []\n",
    "data_num = 1000000\n",
    "curr_num = 0\n",
    "with open('./data/starting_Time_haikou.csv') as f:\n",
    "    while curr_num < data_num:\n",
    "        data = f.readline().split(',')\n",
    "        curr_num += 1\n",
    "        if not data:\n",
    "            break\n",
    "        try:\n",
    "            X.append([eval(data[0]), eval(data[1])])\n",
    "            Y.append(eval(data[1]))\n",
    "        except:\n",
    "            break\n",
    "X = np.array(X)\n",
    "Y = np.array(Y)\n",
    "\n",
    "print(len(X))\n",
    "print(type(X))\n",
    "print(len(Y))\n",
    "print(type(Y))\n",
    "\n",
    "print(X)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4c88655",
   "metadata": {},
   "source": [
    "### 聚类数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16051206",
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4c09d58",
   "metadata": {},
   "source": [
    "# 构建kmeans算法"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "780e47ce",
   "metadata": {},
   "source": [
    "### 使用肘部法则确定最佳k值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "287ee6fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "inertias = []\n",
    "spend_time = []\n",
    "for i in range(1, 20):\n",
    "    clusters = i\n",
    "    k_means = KMeans(init='k-means++', n_clusters=clusters, random_state=28)\n",
    "    t0 = time.time()  # 当前时间\n",
    "    k_means.fit(X)  # 训练模型\n",
    "    km_batch = time.time() - t0  # 使用kmeans训练数据的消耗时间\n",
    "    sse = k_means.inertia_ #样本距离最近的聚类中心的总和\n",
    "    spend_time.append(km_batch)\n",
    "    inertias.append(sse)\n",
    "#     print('k = ', clusters)\n",
    "#     print('SSE: ', sse)\n",
    "#     print('K-Means算法模型训练消耗时间: ', km_batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2eb2a1e",
   "metadata": {},
   "source": [
    "### 绘制SSE图，寻找最佳分类数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0787d2c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_X = range(1, 20)\n",
    "plot_Y = inertias\n",
    "plot_T = spend_time\n",
    "\n",
    "plt.plot(plot_X, plot_Y)\n",
    "plt.scatter(plot_X, plot_Y)\n",
    "plt.title('SSE与聚类簇数k关系图')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('sse')\n",
    "\n",
    "plt.savefig('sse.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ce43753",
   "metadata": {},
   "source": [
    "# 构建MiniBatchKMeans算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d4d000c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:0.8064s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:0.8782s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:0.8168s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:0.7903s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:0.7964s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:0.7979s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:0.8348s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:0.8313s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:0.8013s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:0.8453s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:0.8068s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:0.8223s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:0.8299s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:0.9181s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:0.8577s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:1.9240s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:3.8573s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:1.5314s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:4.1855s\n"
     ]
    }
   ],
   "source": [
    "\n",
    "mini_spend_time = []\n",
    "for i in range(1, 20):\n",
    "    clusters = i\n",
    "    batch_size = 100\n",
    "    mbk = MiniBatchKMeans(init='k-means++', n_clusters=clusters, batch_size=batch_size, random_state=28)\n",
    "    t0 = time.time()\n",
    "    mbk.fit(X)\n",
    "    mbk_batch = time.time() - t0\n",
    "    print(\"Mini Batch K-Means算法模型训练消耗时间:%.4fs\" % mbk_batch)\n",
    "    mini_spend_time.append(mbk_batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "259a0891",
   "metadata": {},
   "source": [
    "#### 算法速度比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "37189f59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_X = range(1, len(spend_time) + 1)\n",
    "plot_T = spend_time\n",
    "plot_Tmin = mini_spend_time\n",
    "\n",
    "plt.plot(plot_X, plot_T, c=\"#ff1212\")\n",
    "plt.scatter(plot_X, plot_T, c=\"#ff1212\")\n",
    "\n",
    "plt.plot(plot_X, plot_Tmin)\n",
    "plt.scatter(plot_X, plot_Tmin)\n",
    "\n",
    "plt.title('K-means算法训练时间与聚类簇数k关系图')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('spend time')\n",
    "\n",
    "plt.legend(['K-names','Mini Batch K-Means'])\n",
    "plt.savefig('spend_time_2.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cd01fe7",
   "metadata": {},
   "source": [
    "#### 选择最佳分类数，重新训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ffe217d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Means算法模型训练消耗时间:  6.991886854171753\n"
     ]
    }
   ],
   "source": [
    "clusters = 5\n",
    "k_means = KMeans(init='k-means++', n_clusters=clusters, random_state=28)\n",
    "t0 = time.time()  # 当前时间\n",
    "k_means.fit(X)  # 训练模型\n",
    "km_batch = time.time() - t0  # 使用kmeans训练数据的消耗时间\n",
    "sse = k_means.inertia_ #样本距离最近的聚类中心的总和\n",
    "print('K-Means算法模型训练消耗时间: ', km_batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2915fcb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\anaconda\\envs\\Mlearning\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:888: UserWarning: MiniBatchKMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can prevent it by setting batch_size >= 4096 or by setting the environment variable OMP_NUM_THREADS=1\n",
      "  f\"MiniBatchKMeans is known to have a memory leak on \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mini Batch K-Means算法模型训练消耗时间:0.8194s\n"
     ]
    }
   ],
   "source": [
    "clusters = 5\n",
    "batch_size = 100\n",
    "mbk = MiniBatchKMeans(init='k-means++', n_clusters=clusters, batch_size=batch_size, random_state=28)\n",
    "t0 = time.time()\n",
    "mbk.fit(X)\n",
    "mbk_batch = time.time() - t0\n",
    "print(\"Mini Batch K-Means算法模型训练消耗时间:%.4fs\" % mbk_batch)\n",
    "mini_spend_time.append(mbk_batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "931ae706",
   "metadata": {},
   "source": [
    "# 预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "18b07ad0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 2 2 1 2 2 0 0 2 1]\n",
      "[4 0 0 2 0 0 4 4 0 2]\n",
      "[[110.35184086  19.99660344]\n",
      " [110.28812848  20.00867817]\n",
      " [110.34004607  20.03615429]\n",
      " [110.45864776  19.95521762]]\n",
      "[[110.33661392  20.03442706]\n",
      " [110.46197636  19.95124545]\n",
      " [110.29505786  20.00490643]\n",
      " [110.21218095  20.03736667]\n",
      " [110.35703325  19.99796383]]\n"
     ]
    }
   ],
   "source": [
    "km_y_hat = k_means.predict(X)\n",
    "mbkm_y_hat = mbk.predict(X)\n",
    "\n",
    "print(km_y_hat[:10])\n",
    "print(mbkm_y_hat[:10])\n",
    "print(k_means.cluster_centers_)\n",
    "print(mbk.cluster_centers_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aa5f340",
   "metadata": {},
   "source": [
    "## 获取聚类中心点并聚类中心点进行排序"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "681f3a13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-Means算法聚类中心点:\n",
      "center= [[110.35184086  19.99660344]\n",
      " [110.28812848  20.00867817]\n",
      " [110.34004607  20.03615429]\n",
      " [110.45864776  19.95521762]]\n",
      "Mini Batch K-Means算法聚类中心点:\n",
      "center= [[110.33661392  20.03442706]\n",
      " [110.46197636  19.95124545]\n",
      " [110.29505786  20.00490643]\n",
      " [110.21218095  20.03736667]\n",
      " [110.35703325  19.99796383]]\n"
     ]
    }
   ],
   "source": [
    "clusters = 4\n",
    "k_means = KMeans(init='k-means++', n_clusters=clusters, random_state=28)\n",
    "k_means.fit(X)  # 训练模型\n",
    "\n",
    "k_means_cluster_centers = k_means.cluster_centers_  # 输出kmeans聚类中心点\n",
    "mbk_means_cluster_centers = mbk.cluster_centers_  # 输出mbk聚类中心点\n",
    "print(\"K-Means算法聚类中心点:\\ncenter=\", k_means_cluster_centers)\n",
    "print(\"Mini Batch K-Means算法聚类中心点:\\ncenter=\", mbk_means_cluster_centers)\n",
    "# pairwise_distances_argmin：默认情况下，该API的功能是，将X和Y中的元素按照从大到小做一个排序\n",
    "# 然后将排序之后的X中的值和Y中的值两两组合；\n",
    "# API实际返回的是针对于X中每个元素的对应的Y中的每个值的下标索引\n",
    "order = pairwise_distances_argmin(X=k_means_cluster_centers,\n",
    "                                  Y=mbk_means_cluster_centers)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e2dc3bd",
   "metadata": {},
   "source": [
    "## 画图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "49864d59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1728x864 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(24, 12), facecolor='w')\n",
    "plt.subplots_adjust(left=0.05, right=0.95, bottom=0.05, top=0.9)\n",
    "cm = mpl.colors.ListedColormap(['#00FFFF', '#8A2BE2', '#DC143C', '#FF8C00', '#228B22', '#F08080', '#32CD32'])\n",
    "cm2 = mpl.colors.ListedColormap(['#00FFFF', '#8A2BE2', '#DC143C', '#FF8C00', '#228B22', '#F08080', '#32CD32'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1743dd0",
   "metadata": {},
   "source": [
    "# 子图1：原始数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "08f36ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iRqVhy5kCzrwyq9X96xu0ZJfXEdWNdcDHsyv460Qed46PIMDNodvO+2/jooUggtwdCHJv+x8niiJ1DVqcWilq/3hHMu9vS+aVBf24aaS+P7++QYtCJjH7wtjoPCrr1PzfliSGR3hy2cBAqpVq7vgujsbS3rGRPmw6XXBxL7KTMf0sVSnV6BCwTNXpuX/VMeRSgdhnp7WZ5/j+YCYv/nna+Ni0HbunT1MeQaXRciK7ksGh7silTYvXu76PY19KCW9eGcMNI0LP5611mJf/Os2xrAoaNDpevbx/t5zz38glXwXx4KpjxLy0mS2tfJlP5FSi1Ymcyq0E4FRuJYNf3cKCT/Z112V2OzUqDdd+cZCbvz5stSW2q1l7LIeVhzJ59nd9k4KTQsa4Xt44KaTIpQLeLv++etQGjbm0j4Nc/15vGhmMj7N5wk0AIrydcLRrOxGna1aL32h8+/g58ddD4ymo1Cf/opds4tovDnLnd0es7t/8OF3JNUND6BPgypyYgG4757+RS74OOL2kFp0IWWXWS7pqVBrun9STzJJqzuTpDfCec8Uo1TqySuvQ6sR/pRecXlxLbLq+fC6vop4ePs7dev5pff3YnljEqB76WKdEIvDd7SOY/u4u8iqVjInw5IdDWd16TV2Fm72UVy6PMWoFA7jay3nn6gGotSLzBgayYFAIz687RU55HfUNWl5b0J8bR1mqpT35Szy/HdM7E30DnNnwyETG9vJGYnCmTU382cJaVh3O4tm1J82Ose9cCSqN1lhlsfyWYWSW1tEnoPuEeG4cGcqNI7vH2/43c8l3whVUKknIqWBqHz8LQzri9W0UtVFC5mIn5fax4VQotaw6nMnlg4J455qBANSqNKw9lsuESB9CvRxbPc6lyA+HMlHIJFw7LOSinL+kRsWt38bi7WzH8kXDuP+HOLacLW57x38Y71w9gGua/Y2v+uwA8ZnlCMDz8/pyJKOMjacKGBDsxp8PjjPbNq24BhERO5nUQo84Y+lcAFKKarCXS3hjw1ljQ0hrzOrny+c3D7+wN2aj2/jHdsL5u9nj7+Zv9bW2jC9AtUrLhzuaNGV/i89hfJQPlw0M5KMdKXy+O5Uhoe78fv/YC7pOlUZLalEtfQO7zwtZaMXD6kqyy+pwVEiN+rjnCqo5lVuFIEBFvZoqVfeHQroaHxcFDnLLMEJSgb78TCIIvPL3GQCGhXswItyTG748xIuX6adlpBXXMPXd3YjA5YMCUcgkxlDGiRdn8PnuVKb09iXKz4WSGhX7U/RVD29c0Z9TuVWsirW+ijiTV83upCLe3JBIDx8nPl04tAvevY2u5qLFgA+nlbLom1j2JZfQoNHx2t9n+HpfusV2GSU1XPnpfm786pDxudIaFXGZ59+9Zm9YSg4OdcdBLmV4xIVXYjz6y3HmLNvLZx0QEP8ncSq3kkn/t4sZ7++h3iCoM7qnF69e3p9PbxyCt7MdK28fQR//f5ciV3F1A0+vsax2+GrRMKb29uGHO0fgqNAb6DqVlk2nCziYVsofx/SddXKpxJim++N4Hm72MoaEuJGxdC6f7kxh6cZEnvjtBLsSixj95jajYp9WJ/LSZf2I8rG+MmvQaLnl2yMkFlaz4VQBG1sZOGrj0uWiecArDmSw51wxUkHfX7/cYHz7BLgQE+SGi72cn2OzWPx7U/xr06kChoS6c+Pyw6QUWQ5XbA8SQT/mpk+AKzP7+XP21dZLfM6HY1nlvLb+LNcPD7FYuv6bEASBm028cLlMSrLh/9Lb34VqpYbcivqLdXmdRpiV8NTont6MNtSQ73lqMt/uT2dmP39KaxrYeCrfOC0jxNOR3+8bwzO/nySvsp7imgaKaxp49OdjHMuuwMNRztTefty24ojRUDsopNw8OpxjWeWcK7ae+yioNq+fj/gHhtBsdIMB1mh0DH19K0qNljvHRlCt0vLyZf24d2JPJBKB28eGE+nnwsx+ftSqtNz41WFigty4bWw4SzedNTvWy+tOUlDTQKjn+X/YdCKcya9m/NtNsTgHuYBWB38+OI5l25MZH+nDDR1MMLx/3SAenKwPQby47hTxmeWotbp/jQHuH+TGzscn4WQnxUHRcmZ/fKQ3B1NLeWJmFFN7+xHxzIZuvMquYUioe6uvezvb8eTM3sbHk3v7mu8f5sHmRyfwW1wWT64+iVSAtcf1HrJUAgND3LCXS6hX6whws2Pf01MByC1v/83rx0NZPDQtEl8XywkdNi5dujwJd/f3R9hypsj8ufERPDu3r8W2X+1N5fX1iR06fmdiWn85OdqHQHcHXrmsHw1asVWj05zssjo+3ZXKgkGBxiqB/yqN2gnWmBbty7akohZfv1T47Z5RDL+AzraPtifzU2wWwyM8WXc8j2APB4qqlGZ6yH0DXVl23UB6+ZnnEH44mMGSdaebH7JF3rt2IFcOCW57w25iX3Ixv8Rl8+L8vng7/3dvDhctCXfTiDALA7z+ZD57U0p4eEokT69JINzLkdeuiGHphotnfMFcIHynYVZZSlENR7PK+fbWEYyLbF/bcoinI29eGdMl1/hPoriNJKlGbO/IzIuLTHphqZJVsVnkVyrZmVjE1UODmdHXD61O5L4fjxq3OZNXxbT39wL6sjJHuZQREZ783mxqSFv8eSLvkjLAd6+Mp65BS15FPWvuu7BE97+RLk/CTeztS+rrs7liUFPBdm6FkrP51Xy6M5kqpYaE3Cou+3g/1gYkBLpe3EKNE1llqLVii3XINqyz7nguw1/fxsRWblq7zrWspXAp4diB1Y81gg0dn7UqDU/NiuadzUmsPJRJ42HlUvPyyju/i+Om5YfZc66YE9kVFsfzcrC8HgEY29OLJXMvLSGoXr76+vTRPWyaK9boFusmlUpQ6/SJma8WDeX2b+NIL63lZF612XaLZ0axdLO5gHeAuzNSqZKccmULTZ9di1ILb181gKuHXjpexT+BRu+3vL6B16/oT055HT/HZlNZr+7yUUOdga+LgqLqBpwUUiJ9L6zJZfktw1iy7hTzYgJJKaohuaiG1OIatj8+keNZFeRXKXl7U5LZPiJw+3dxLJ7dm5UHM8mtqEcOeLvZ46SQUlqvF3wX0I8k+vmeUUT6XnoVKM1rom2Y0y2NGEq1ln4vbkarEwlytye3wnL44iOTe7HuRC4ZZZaJB9PayYvFDSNCeG5u324ZtPhvQBRFDqSWsvZYLqvjcxgU4s5xK97cpcqNI0L537Re+Lp2vtDML0ey8HKyM84nfOb3BH6KzW5x+0EhLhzPriY6wJmkfOvVPwFu9hx8ZmqnX6uNzqGlGHC31AHby6V8efNQlsztY3XUy5c3DeTRmdGoNNaVs1bdOYInZkR17UW2wU+x2fwe/+8SGO9KBEFgbC9vRvXwQiYRyC6rM2vlvdTwc7Hjp7tGGq9xVWwWo5fuJLmwuo09O851w0PNhsO+MK8fYZ5NCarmf6Xj2dVcNzSY6tqWpVsvpDLIxsWj274RU/v4cef4Hvz98HguH2Su4O/v4cwvR7LIr7I+Nj69pI77JvZA2kWSDoIAMolArzb0FF748zThi9dTUXfxNIz/aVw9NJjeAS6U1jawYOClO7mhuFpFsIcDI8M9iPDWGzOtTuRwetfr8ToopKy6e4zRObG21gvxciSvquXP3bvXDuySa7PRtXS7S6IT4frhofiZ3LBvX3GEp9eYC47YS2F4uAcKqYRgD0dGvLHdapKuM5AIAlpRZHpfn3ZtP75ZP7+N1nlpfj9uHRPOlGb1sZcSOmD827vYm1JKjUrLC/P6cMvoMK4e2j113HKp0GKOY1iIK1cNCcbXxQ4/Vzur2zy1+gSf7Ehm+b60rrtIG51Otwc0b/76MKdNxrgsv2UYP8dmsTOxyMzA6hAoqVby892jGBLmQVV91wl7r7htOIu+juWz3ekI6D1iLyc7ov2c2Jdq2fJcrdIQvng9TlJ4/eqBXD7YlqBrjWHhnrjay1jwyf6LfSltIgCPT4/i+m7S1W3E18UeT0c5ZXWWq8Dx0X7c80McO5+YiJOdnGNZ5cilEvoHufHHsVye+O0Eno52vLNFn8DOKavnpcv+GcNQ/+t0uwfs4WiuE3uusJrltwznl3tGE+TeFAdr0Iqkl9bzwfZzxGWUWV2WdQS7VsIX/QNdGRbugaeTAhG9l15coyLCp/UETK0W/vfLCdJLWh9BXl7bQGZp94wpv1Q4mVNJanENSQXVLF6TwNWfH6RefenX/YpAiOfFaRjY/tgkRltp3Hl/WzIJOVVMf38Poijy6C/HmffRPtYn5HP54CA+XzgEL5cmz/j3oy0n9GxcWnSbB/zc2pOkFNXw3nUDWROfw3tbkwEY20tfHzgs3JOf7x7N5Z/ux1EukF2uL2M6lVvJoq8Po73A2iVVK7sPeXUbnk4Kymob6BvgzBlDpvmPY+2bb3b7N4fY+ZT1DLRaq2PWB3soqlHx4x0jGdPLm5f/Os2p3EqW3TD4XznO5VRuJZd9sg97mZRh4R7sTf5n1Ps2ctPyIwwNdWN0Tx/O5Ffx3Nw+9GyWHyisUvLhtnNM6ePHtD5+LRypY6SV1nIwreWYc4SXEydyKskwTF9OLdZ/Th//LYHKejV+LnYUVqvMGopsXNp0iwFWqrWsis1CFOF4ViU3jwpn46lCXO1l9DZRz1p3PJfSmgbkrvaEeDiQXV5PWa31xFxnIgLlhsTa+ChfzubXIAIrbx/OoDDPVttpAdLLlDy75jhvXDXI6uvVKg2iCB/vTGFIqAcr9mcgAgdSSrnqH15fvOlUAXEZZTw8LRJXe/3QShd7GY5yKa4Oci4fFERCTgWVXRhC6grisyqJz2oS+J8bE0BORR11Ki3f3T6CN9afZd2JPH6KzWb/4ikEGpotZn2wG40Wtj0+0epx1Vqd2TihRjJLa7nqswMtXs/6h8fRL9CNGqWaKD9nFFIJ907sCcDCUaHsSynluTm9+XZ/BtcN/3foj/wX6BYDbC+X8v61g0grrqGnjyP3/RBPiIcDn9w0xOzDOCcmgINppUzt7cfGU/lkl9cjl8D5rlxbnthlyRcLhyKXSfTjfQQYGe7JIMPA0GdnR/PWpiRGRHhwMK0cmURA08wjX3Ukl1VH9G2jjSLboJcjvG1sOJ/sTCXMy4lj2RXGaxoY7Nbu91KtVLPtbCERXk6IwMBgdyQXcdJHeW0DHk4KnvztBNUqDUHuDkQHuNAv0I0wLycOPjsVuUSCg0JKanENn/6DZTo1OpF1J/KMj0/lVTK5ty9/JuRhJ5UYP8P/++kYiQV6r/Tm5YdYeecos+Osjs/hydUnuGNsBEvmmWuh2FvRHDalX6D+s7IjqZhzhTU428mMHXRPzuzNkzP12424wGnMNrqXbgtBXD44iNkf7mHZjhTjc/kVSuMkirKaBqa8uxuAcE9Hvrl1OClFNQwO9WBfcjFHM8v5dFcqyg40ZIg0dTS1xfR+etH33+KyEUXIr6xn8ZoE7hwfwd0Te3H3xF4AJORUUKPUcOPywy0eq6BSib9bUxzxyZm9uW5YKEEeDmh0Oq4fHoKbg5yeHeiweu3vs/wS1xTbu31sBC/MtxQ06g5eNWg3L5nbh9vGRXAwtYTcynpe/vsM43p588OdI43eMMCTM6P/0QbYFC8nOZOifJFIBCZG+SAI4G7Ia/Tyaxqg2ajra8q5wmpEEZKs1BY/8lN8i+cMNPks9fJxxtVexsAQd9sU8H8B3WKAy2oaqFVrjDErgEemRpqNATqTX2n8fW9KCa/byxlsGOk9LtKHcZE+/Hg4C2U7pmCY0h7jC1BVr+ap1Ql4uyj4+a5RvL/tHD8fyaZereXD6wcbtxsQ7E7kc61LLI56czsA4V4OFFSq0IoiWx6diFQiIJVIWXrVgA69B4DeAeZtphrdhSe03tqUyPazhXxw3WCzSR47E4vIrajnppGhVr/kjTKJeRVK/U1gehQ/GSY31DVoEEURQRAorVEx7q0d/4jW4/ZSWqtm8+kCZscEWEw7fnBKFB9uT0GtFRkWbiny/9j0KPoGuDKml95LzSippbS2gRBPBw6lV7R4zrqGpvBN30BXEl6a2TlvxsZFp8sNcEmNkmGv6Q2Sn7McLQLzBgTw6HTzzjYRfUmGRAI7n5hs9VhKTcdG3vg6y5kVE8CWU/kUVLceSx766hZjqOOhKZHcNjYcjU7kumZ6vk+vPoG6nUmOjNKmtuqrP92Pi4OcT28ael5ji24bG4G9XMrXe9MYHObBC/Mu3PtdE59DUbWKfSnFxmvafjqPO1YeA6BWqSHE05E5A8wn3759zQCuGRZspg53w4hQXvrzFEezKlgdn8OCQUE8+duJf0TlQ0e578ejDApx4+XL+jMwxN3stR1PTOJkdqXF3wz0YYbLBweh0eq474d4Np8uaNfNqb0qfDb+eXR5GVq9qukLWFijpqSmgRUHMi2223AyHx2g0cHRrHKL13U6sVVVqsZXBODWMWGcfWUWsUtm8MqCGP56eCKKNtro1Dp9N9ziWdH4udozq38Aa+4bw5he5h/+X+KstyPbtdFmW1qnJqO0ji/2nP9SfGiYBynFtfwWl8O5wvObCGLKh9cP5uZRYUT6ulBQqUSt1RmNL8CbmxK5f9VR1sTrQx+VdWqqlGpc7eVM7eNnnMr79b50Br+yBZVGb02eXJ1A1JKN7Ej69w3obOR4diXvbztn8XyIh+UNqzmF1So2nmqf8ZUAc2OaOghzyuv4KTaLWpVlUnPphrMs+sY8NKbqoNNio3vpcgMc4uXYYveOKU/P6s2gEDcmRvkQE2SZnKpWaSiobAo/NLenph+zFQcy2XS6aUaWj4sde5+ewoHFkxkU0nLiS6MT+cgkRm0NfxeF1ee3PjoBXxc7q1oXpszpb33AaGucyq3kpuWHOJFdwYQoH8ZHehPh7dT2jm0glwqsPJTJbSuOMPrN7Tzy0zGr27k5yMmvrGfsWzsY8+YOyg2aBA0aHVVKNd/uT6fcSgPBv52ZfTv+vwTwcJS3uc3YXl44KaToAJVJ3uOxX07wzO8nWbY92Wz7gsp6Pt+Txp5zJTz8k15n+Ms9qUQv2cQnO1v/TNu4eHRLI8bhZ6eR+Moson31MV9/KwbZ3VHBHw+M47vbR1jNCLs5yJnZT19vGeblSJS/C/Zyy8sXAalEYECwu9nzX+9NY9zSnXg6WjegjdQ2aDmTV9Hi61sfm0hj8YG7ydvYcLKAompVm1UXv5ok0j7ekcxDPx2jsg3jtTo+h/0ppXy7P4Pvbx/ByjtGdmhCR0tITaooRGDDKctx6O9cFcO0vv4o1TpqVBpqVBrmfrSXQ6klxLy0mSGvbOX2sREXfC3/RBYMbp+2xc6kIuZ9tJe/E/SVFDsSrdeX20nhjcv74+dqR/9AN366exTvXjOQy0w0NIaEeaCQSSw+354OCuPncnykvqU+yVCRca4LBIVsdA7d2IoskmaIiT42I/q8jvDB9YO57ON9bS6/HeQSvJ2brOPaozl8uVc/9DPQwwF7mYBSIyLBUvhEAKL9W/aSZVIpjgoZNSoNX9wyipE9vCioVBoTb40ceXYKw9/YYbH/9sRiIp9dz44nJ/F/htbRaX18WTAoqMVz3jY2nGqlhgWDOlfMZnCoB3/cP5pbvz1ChZU63b8eHEtMsDuFVUrCTNS2apQaXvrrtNEzW7rxrMW+/wX6vbCZT28awuyY1kMOa4/mciq3it/icpg3IJAZvf1xkEupV5uHB6b09efr/ekUVqlYHZ/DM3P6WBjaxbN7s3h2b5qjUEhJe3Ou2XMvXtaXCVHeTIrumAZHQnYFd3x3hCFhHnxxs4WCoo1OpNsM8IR3dhqTV6/8dZqcsloem2H5QWoNe7mUMC+nFg1wo6rZj3eMxM2haZl3zmSC8v+mRuLnYs/h9DKOZZRQ28zupC81/xA3x0Eh5a+HxlFao2JYuCcZJbWczqtkam9ftic2jV4avdTS+Dai1sH4t3Yxtbcvnk4KqyI1Op1IanENPX2cCfNy6jK1q+xypdH4Rng5kl7aNPlj/sf7ifC0I71MxfwBAay6cwTLdqTw+U2Dmfh/e4zb/Vc7r0Qgs4VJKRtO5pNaVEOUvwvujnJuHBnKwpH6SckKhZRBIW4cTDPXGdl4Ur8CmRTlw82jwyyO2VFc7eWt3thb4pNdKRTXNLD5dPs6QW2cP90Sglgdn21WDlaj0rJsR+p5jSz/5MYhDAx2o3l1lJNCiiiCWiuSbSiTEkWRNfE5LBodxuMzIvn21uF4u9jz0NRIvlw0lBtGtr50zi6r474f4vn9qHniLcLbyVhmdOPyQzyw6hjT+5q3o7anXHl7YhEFlfVWRd5fXX+G6e/vYemm9s/JU2m0dERgH2BQqBtSwx/T1Pg2kl6mj7v/lZDPjctjSSuu5ZNdaYzr1XbBv4OVENG/jTvHWX6GlGotD646yrtbz/HgqqN8fzCTmCA3s+qXB6b0wloo2Ekh4bOFQ5naSntzUn4lk9/Zyd5zXTPQ9IkZ0YR6OtqmwHQDXe4B16o0rD1qfbCgl1Pr8VhrKGQS1j04jkOpJdy4/LAxk1zboGXR6FBEUWCeIWb2yM/H+PNEPs52UvY/PZUyEx3fEa9vo0ZlvgSc2suTkhqVMXyx5mgOG08VcCK7osVBh0pDmVVtg5bEV2by9f4MzuRVsv6kZTzVGntTSrnlm8N8f4d511TjBBCVuvUstiiKfL47jdJaFd8fzGRkhCcr7xjZrnMvXpPAr3HZOClkVKs02MskbTa6FFWr+MoQzmmLf2MJWnO+2Z/OLWPCjRUhoF+pXTUkmOSiGgLd9KstL0c5eeX11Km15FfWc/PXsVaPNzjEAweF1FhL3RxRFJn14T5E4OZvjph1XXYWkX4u7HnKeimojc6ly12Uo1nl7E+1FBjxd1VgJ5Pw5G/HWZ/Q/smvSrUWtVbHqJ7epL0516zq4J4JPXj18v7Gxw5y/f1FJpEw96O9THl3FwdS9MIwtSpLw7YrrYxhr23j+4MZAFw1JJg5Mf48OavlmLWDIWHobCfFXiHjgcm9eP+6wYwIc7fY9smZ1o+zJ7mU8MXrGf7aVl79+wzrjuXy4+EsBAF+OpLN7nMtl3PFZ5bz1qZElu9Np0GjI7kd5Wk1Kg0bTuZzJKMMnQjDwj2YNyCArY9OxMel7YoVG028sSGR1VYmpbxzzUD+eGAs9nIppbUNPPrrCca8tYNp7+3mUGoJ1rrIh4W588Ndo9h0qoCoJRt5Yd0pi232JJcYE71tDShYHZ/D3GV7jZ95G5ceXe4Bj+rhxV3jI0jMr2JvSpMhLq5R0+f5jSg1Ir/F5wICcwfoPVedTqSwWmmhFJZXUc+cZXtxUsjY8ugEnOxkJL46i7nL9jK2lzdBHualWW9dPYD5AwPoH+jG3I/2AU3aEM/MjmbZjlRqTOopPexllNRpKKjUz6yTSQWemd2HkBbGvWh1IreOCUMiSLjGRLhbIZPw5KzeXPPFIeNzT8+K5qZRYRxILWF/inXFq+KaBr7e1+RdiqLeE07Mr2JilHWx+N4BrkyI8sHZTsq8AYH0CbBs8jiRrW+OkEqwqMEeGurOTkO9rp+rfZuj5G1Y0poc6fxBgZzOqzSbqv3JrjRuHRPGppN5Zg1CiQU1JBZUEZehn8Qdn1lu4QlH+7nQ08cJB4WUdQ+0PvDy17hsTudVse54nkU9e3s4mlnGvT/EExPszte3DO/w/jbapluGcqo0Wq75/AAJOVUtbvPM7N7cY1B3WvLHSX44lMWzc3pz94Sexm1O51Uyd9k+JAJsemQCUf7tnwJbUddAaW2DhaygqdJZgIucN64exLhe3lTWq5n49k7UOpGtj04gzMuy7vbXI9k8tSYBb2c74pZMY2diEW9uPEukjzPrDSVdCqnAzH7+vHvtIGLTS1nYwtKzJR6fHsk9E3td0Dy16744yOH0Mlzs9KEGazgqpEzv68vGkwX/2aTahbD/6SkEebQsLZpcWM0za0+SX6Ekt6IeO8Og2ca/tFyil45qjNq4OciorNdw29hwXpx/fuLqJ7IrWHsslzvGRRDk7sCtK46QWVrLj3eOJNjDulPxv5+PsT+1FJVaS61KYxySsPreUQwLtwn9nC8tDeXsliqI7w9ktmp8ARaNDjf+XmYo9C+tMddx6BfoRoinA9ll9fx+LNdqOU5LuDsqjKIpLVFQrWayoWRHQD+qCHScyK6waoAj/ZxxlEvQ6HQMemULygY1Sg1mVRqvX9Gffcml3L0yjj3n0Rn27tZkPtiWTOySaTgpZG2qZlnjhhGh1Ku1XDcshO8OpFNaq6a02YDHPn5OrDue38IRbLTFV3tTeemy/i2+Hunnwup7x1Cj0rDxZD7PrEkwqxlXN2uLa5TvbPwOPLAqnvUJBUT5ObPlUetSl80ZGOJOoLs9Y5fuQAfotCI69HXB1gzwt/vT+eN4k+qbaZhk5aEsmwHuArolTd3WxAiAx387bhQdeefqgfxwx0iLmOm6Y7lkG8bWT4puWlIVV6vQ6kRKa1RotO1P/CQWmN8Ugk0mcng52/Hng+MQEHj45+PEZehLhradKWTK/+3i1yPZDA714PiLM1GqtVTU6Y1vc97elMS6E3nsSirGpbWxHK2gFWHoq9vo/fwmHvjRXDWrqErZ5v6XDw7izwfHcdOoMH6+ZzQHnplCTJB5qCI+u/UbpI3WWXEgk4Y2EqYAznYyrhkWYpSSbI27xkXwlkG4aUOCfkXV0Rb0Taf0KxqNVuSKIUEsvTLG6GRodSIrD6YTvng94YvXczqvSRDLx1nBXeN7IBH0q7jG5g4bnUu3eMB3T4hglUEtqyU2nCxg4cgwxvTyxslOZlWAZMuZprpEnU4krbiGuMxynlqdwPBwD+Iyy5kc7cs3t7YvXuXuYO4RPznL3KMO9LAn0N2BkmoVXobKiI2nCkgrqeWvhDyuHR6CQibRT/JoYd7Z07N789zaU6g0OuYPDOGR6VGsPJjBnyfyjJMNOsL6kwWsN4RNGvWOn5wZzQOTe7W4z7nCauxlUjacymfpxkSkEn29tI3O5c1Nidw8KozX1p9lWh9frh4aYhE6WhOfQ2mtinpN22GecVE+xo7HywcFsvZ4Hn0Mqngf70imrkHL4zOizToaAa774gCH08vpE+DCxkcm8FNsFmqtyNtXDzTb9pcj2Ty/7ozxsau9nL4BLrg7KFhx+wgUMgmPz4i+oPCXjdbpFgMc7u3MkFA3jmZVWn09zMueWf0DGRFhKeEHetnEFfsz+L+r+1Ncox8ffuu3+lj0rWPDAb0XLIr6UTHtxd/NniVz+/Da+rM4ySVMiPRhdXwOMUFuRPu7YCeTsuXRCWh1onHp/9iMKHxd7bhisL7AvUqpbtH4AqyKzWLBoEB+jcuhd6ArPi52PDYjmgcn9SLqhU2tXp+DXMqsaHfWnrKetGv8Cn+7N5V7J/aktFaFm4PcrCTqXGE1sz/ci51MQoBBV1ar44JHPNmwxF4u5fejuexILGJnUhFf7Elj8/8mYC+XsuFkPg/+eLTdsw37Bzrx5Z5UjqSX8cTMaO6a0JOcinouGxjIyZwKYxfl1D5+DA3zMNs3LqMCgLP51RRWKdnwyASr5zAVqBKAp2ZGY68wNwk249u1dIsBziqtNTO+fi4Krhoawhd70vBzsWPn41MspjvsSy7h5b9Oc+vYcI6kl1Gv1pKQW82v94ymqFrJVoM3fPvYCMb18iYmyJUz+dVEdyAxBxjnlUkkAoNe3QpAgJs9B5/Rz3iTSyWYhl2D3B142uApFxgEalrjaGYFq+8Zw5MzextLvL7cGMsbu9uOB9ertS0aX1NK6jT0fNZSo/jqIUFU1qtRSCUoZBJSi/9bg0G7m892pbLv6ckk5FSwJ7mEgkolKrUOe7mU1fHZbRrfp2dG8X+bzxHl78R1I8J5/o/THEkv54mZ0fydkMeRjHKKqlXUKDXIJAITo33oZ0Xa9PEZUbyzOQkR+GZfOs/M6WP1fFcPC+H5daeoV+uTgZPf3W383Fcr1Vz7xUF8XOz4/vb21ZXb6DjdYoDnLNtr/H1sT0+enNWHQSHuPDItErlEYnW0zsZT+SQX1bD2aC7vXzeI3eeKjV6nr4s9ew2F4u6OCuP0ibG9Ol7D2lhjW22oC5ZJBIY08yiaI4oiR7PKueqzg+06xzubE5nc289ogNtjfDuD1SYNMON6ebH1bBEyCeh0lhoYNjqHh1cdI8TLke9vH4Gvqx1uhna3kRFe7Ehs/f8+PMKLVENjRY1KQ3pxHQMN6n23jAmnSqlmVIQXT6w+gQgsntXbalL2/sm9cLGXsTo+h9kxrSu2nX11Nrd+G8uupGIz4fdv92dwNr+as/nVnMmrpG9g+8dn2Wg/3WKA5/YP4BdDsfqnC4cZdRrsZFI0Wh1ncqvoE+BqFp96aEokznYy5g8MJMTTkYWjzHvj26poaA+FzVqhZ/T1Y1K0L1cNDSKjpBZne5mZqE8jn+9O460OtAh/tjuNz3an8fdD44xhgO5m61l922oHJjrZOA+OZldwNLuCCZE+TDCp3W4ep22ORIA7vj1Mv2B3Vt01Gmc7Gc/M6c2fx/NIL6klwtuJ1y6PAaBfkBsqjZZIP+urveJqFXkVSs7kV/HGhrMUVCqprFez/+mpONtbfuWX3zyM7w5mMMNEKvX64SH8fCQbD0e52eBcG51Lt9QBA2i0OmRWpsG+uO4U3x3M5NYx4bx02fnVOzanoFKJnUxiMTKmOX2WbLCaDFk0KowfY7Nwd5Czf/EUCy/juwMZvPjnaYv97KXgaCfXewuiyMH0MjwcZOhEfSXDhkfGE+hmz5O/nTDzTm38e1BIIcTTiZ/uHoWvi/5mm1pcw/yP9lHX0HKVhJu9lEql/vWDz0whwM2B5XvTeG39WXr6OLH98UnGbZdtP0dCdgUPTYtiYDO1NIA7vzvCNsMNt7GeGMDDQcaxF23jjC4GF7UOGLBqfAFjl09bHkJ7SSmqZs6H+3Cxl7H7qclWhW4a8XdzMBOgcbaTUt+gZfvZQnQ66734oF8OTor2QasVmfb+bnSivt73umEhVt+nWqszS+RN6u1rM8D/Ujyd7MyMJUBOeX2rxhegb6BeHc1OKjD6TX1e4dtbh2EnkxiN7NOrT5hNZNmdXEry67MtjhXu5YQA3Dw6jFtHhzHlPb1yXXm9Bp1OvKjTtDuTj3cksye5hLevGkCVUs1lH+uT4U9Mj+LBqZEX+erax0VJcT606ih9XtjE2qM5PD+vLxseHs9zLSQKOopGJ6IVRRq0OnRtePcr7zRPLtw4MhSFVEJupZJevs5seXQCx7Mr2G+llz7Ew5EdSUW4Oyrwc7XjYGopV39+0NjGbIpcKjHzoj/clnSe787GpY5WJ/LEbyd4/NcTRkGliVE+9GhjgkmEtxObHxmPaNKFeNuKOBJfncV71w1CrdFajMNyc5Dx5e4Urv18P9mGVucjGaX8GJuFTCpw+9gIevi68MXCwTjKJYR7ObH5dAFpxTW88teZDgm1F1erOJB6cTUlNp7KY86yPWw5ra+JXr4vndj0Mr7em8qCj5sqkZbtTG7pEJcc3RaCMCVi8XpE9Gpo8c9Pt3h9Q0I+wZ4OFmLU7SWtuAZHhQx/N3vmfLibxIIaVt05klE9zWuLv9qdzOsbLed6AfxvWiRXDQlm4js70YkwJNSdcC8n3rlGX0u5ZG0CPxzORgIcfm4qI9/Yjk6ET28awpxmAt2lNSpyK+qN78e0/dnGvxeZRECjE5EIdMlkaAeZhHqToL6dTIJKo0MqCMhlAlsfnYiHg4yxb++kQSNSr9YilwpM7+vHhpMFTO3ty9ftrJmf+f4ekgqrWXplDNePCO38N9MGh1JLuP4r/bw7O6nA4FAPYg1iUtBUEw/wyNSePDpdX6kUn1mGRBCME9YvFhc1BKHR6vhoRwq+rnbcNDKM4REexGWUc8e4cIttTeOrjnKBnU9Mws/Net96S/Qw0Xs4k6/vHLr+q8Nm0n1JBdV8uMNyQKZUInB0yTTcHBVUKdUEeThQq9JyNKuCo1kVLJ7dmyd/O87uZH15mA7wdrbj4xuHkF5Sa6ELDHDlZwfILK3js3ZMT7Dx7+C+iT35bLf+89VVJdf1zTKqjRNKov2dWXHbCHxd7Vm2LdkYAw5yt2d6X3/G9PQirbiWK4a0X6zdx8WOc0XVeJ6HhGxncPf3R4y/q7Qih9LNxewFAT67aSh9ApwJ89J//9OKa7jm84MIgsCuJyYZRbWOpJeiE2Fkj4vfWt0tBnh/aikfGoYIzujrj4udHJ0IORWWy3VX+yaV6jq1yMg3d/Ld7SNaVAMrrlbxzuZEJkT5MG9A6yN7opdsoI+fE1H+7uxKLLDQAwaI8Hbk012pPDOnD672cvY8ORmNVuT97ef4em86Y97cjmneTgDu/SEenQhvXzUAuZUYsLOdDIkAjoZ4tIud1Fj21tVM6+NrTMjY6DoEYOmVMWw5W8jU3r7cODKM4pp6fj+ax5WDA/n7ZD5KdedaYm8nOYKgVxZsRCrAs3P6sP5kPiMiPLlrQg++O5iBRICdj01CYeism9GvYwNFv71tOJX1aqtVQd1BbYP5zaZvgCuu9jJO5VUS7G7Pa1cMILGgmqJqBQWVpUT5ueDmIMfTyQ6pRK+k+Ozak4yM8DQ2sfx2zyiGR1xcI9wtIYjKejX3/RCPvVzK8kXD+GRnCh9uT9YnroZbLmd+OJDOkj+bWiQ/vH5Qi6NVPt2VwtubkoyKZI0cz66gl68zd313hENpZW0Oy2zOnP5+uNgrAJHXr4ghsaCaeQZJy0akgl7w5GhWRavXqVRrKa9roLhaxYvrTpNbVk1RrW1c+L+FoaHurLl/bJvb3frtIXYlWW+scRXg3ZuHctf38VZfv2ZoML/F5yCXCsbRXlcPCeL/rh0EwJ/Hc1m8JoE6tQ5vZwUlNQ2Eezmy68n2C6u/tTERZ3tZq23t3U1eRT3bE4vILK5i+X69nIFEgPgl082qnNYey+HRX04glwiodSIDgt3488Fx3LHiCIfSShkR4cnOpGJCPRzIKm8qP31mdhT3TIy0ON+CQYFmzuCFclFDEG4Ocho0Og6klvLTkSwemhrJg1N6tVhlsHBMBFP6+vPOpkTWHs9D1cpkhXkxgexJKmZ0hDvPrEng8iFBxKaX8e4W67Hd9pJSXMu5Qn23XWPy48HJvfjhUIZxhtrq+8bwxgb9QEpXBxnTWhgjU1bbwHVfHqSyTk2VNcUeG/9o2mN8ARYNC2jRAFeJML2vP/2DXDiVq0+OOSmk1DZocVJIuXpoMAdSS+kT4MLICE8yS+t40aRs87JBQaw8mMmRzHJKahqQS4QWV43W+ONYrjFkMqan10WPmS7deJZdScU4KqRGB6cRnYhFiWn/QDcC3ezxdbXneHaFsU9g17litDqRstoGrhgcxLwBAbz69xmjDsubG8+ZGeCnViewL6WElMJqXl7QsrpdZ9FtZWiNVQCNEyRaMr6NBLo7UGoY155a3KQAVV7bwL6UEqb18cNBIaW4RsWh9DJjTOinI9lWj9dRCiot59V9ujPFrIPsRGYFp3L1LdYNGh1OLZS8pRXXkl1Wj0QAT0cZAlBaZzPE/zWmxIRx95gyvjyQZ/X1T3elUF3ftDL66IYhnMyt5PoRIfi52rN/8RQAHvnpKGfzqy3CXTFBbhzJLAf08pbzYto/Rbt/oCsyiYBUIhDm2XrFRlcjiiJf7E4zJuqb08vHMicU6efCAUMbdWGV0hirfnRaJOsT8nnnmoFEGRpXJkb50Ou5jQAMbTa5Zni4J3GZZW12w3YW3VYFoVRr9ePNrejqtkRJjYpDaaWM6+nFc3+cprJeTVpJDXkVSqNQ9dYzBS0u27oTAbh2eAhBbg54uyjoHeDKF7tTuWNcD0ZEeLImPocfDqVz7CLKPsqAS8XsPzq1J+9vt0yC/pMIdLXju9tHEOlvqcdgjdIaFY4KGaPf3EpFvWUIamiYBx/dMJh3NidyxeBgs066Rs7mVTHb0Nof4eXIzicno9Jo+d/Px9meWEiDSYJiULAbfzzY+tSMi4VOJ3Iyt5I+Aa4Wgj/Ltp/jva36nFGguz15hlzRwGA3bhwZajVsealz0RsxGkfKd4SyahX7k0t4eNUxC+2CxskW0/v6M6OvH6dyK/FxtuNErnXFta5GRC/v10iUtwPnSurZfLoQqUF/obv1x768eSi/H8th0yl9KOVSMb4yCf944ysAG/43od0t8SdzKrnqswMEuNvz+/3jmPXBbkx7MwKdJLx1VQyB7g68f93gFo/j7dz0ldXo9N+K5MIaNp6yHAJrbWLzhVBUVc/Yt3YiirDziYmEXICn/NbmRL7YncY1Q4N555qBZq8t255i/L3R+I7u4cmPd4761zSRNNJtBvh8mL1sL82n4wwIcuWOcT1YMLgp2fXloqYbS3ZZHbevOEKUvwuf3DgEgJO5Fcz/qGXJSGvIBbiQpPW5kqYQRgc04juVKb19eXbt0YtzcgMC0NPHiRSDEptUELCTSdC00Rl2qeNiL+Ou7+P49Z7RrYbTVBota+JzkUoEGrQ6skvrmPrubprvoZNK2lVhIJc1fWV7+uqX1P0CXbl3Qg++3JNm5qh8dyiTMZE+nVY6tulUgTEB+OeJPB6YfP7dZnKJ3utt3jlapVQjNFuVDwh0Zmpvn3+d8YWLZIA1Gh0Lv9YXVT82I7pFHeDm9ZPLFw1lWl/z8pnn1p7k74R8vrh5KKN6eBHi6cjWx8xHtsQEuSOhYwpgnVkx5KyQMiHKm7iMcvxd7UjIa38H0oUw44PdlNRcXN1fEcg1ET3SiiK1/3DjC1Cl1HAytxK1VkQha9kwrDyYyWvrzxLq6ciCQYGsM4z8af5fKajScDSrnCm9rSdyG4k3xHgBymoajLrO60/lm32+BeBIRjm/xmVz78SedAaLxkTwa1wOGp14QcYX9JKZlw0KtOgQvOGLg2bfPX8XGQl5NSTkJbHtbBE/3zOmw+fS6sROkzrobC6KAX7452PGpNm1Xxzk21uHM7m3r8V2t40L55t9GQDcN7GHhfEFOJBaSmW9muPZFYxqpbDa382OvMrOn/irkApWh1hueHAsvu72eDubq58VVCoZ/eZ2k2GM0EqRxwWRVtzxiRtdQWtVLP9UgtzteXRaVJuC5TFBbrjYyxjVw5OSZjMOTfFykDLBZOyPRqvj17gcov1dzATXJ0T5cNnAAP48kc+Z/Erq1VrsZBLqDHXlN48KpahahUwicDi9jM2nC7h+eEinqAcC/P3w+E45jiAIxqSYKafzzZ2TK4eE8amhOiO33DIx3hbvbz3HxzuSuW5EKG9cEXN+F9uFdGsr8smcSl79+zSxGU13cQnw+wNjCfFwMI796QhJBdXEZpRxzdBgq9qob2w4y/cHM3CxkxoL1k3bFk2xk4G7ox1yiWC1ScQaH1w3iJE9PDmRXcH4SG+mvruHomp9kvD5edbV3VKLajidV0m1SkNOWb2x/KezcVYI+Ls5kHKJGOJ/Gy210rdEj8XrW12F3TgylNcW9EciEVgTn8Pjv53ASSHl9CuzLLad/eEekgqq+foWvfNSUKmktFZFP4Nu7y9Hsnh6zUnj9lKJwCNTI3n4EhepMW3TP/TMFPzdHJj/0R5KqtUcfHZqh44Vn1lm1OzuF+TK+oc65+ZxPlz0JBzA+9vOmRlfhRSOPDeDMUu3U9ugRSYRSHljToeOGe3v0uoUjLiMMpRqnVk8Q0TfRNHccVVpoLCq/V6yAEzv44uTvZwANweUai3VSjU6Eb7el8EtoyMI9bIsmenp60xPX2dEUWT80u3GY3V2sKCmQTR239noXJwUUtwd5aQU1dDL17nN7ZfvSWkzBLbqcBY/Hc4ifelc+ge54edqx5AW6nHVWhGd2BTe8XezNw4mWHkwg092pphtr9WJbDiZb2aAf4rNYn1CPi/O79uitnB388iUXnyyK5WbRobi7+YAwF8PWR+p1Ba+LvaGEV0SPrq+KbF5KK0UnSgyppk2zMWgW7+ds/r7syOxqS1Wq4XT+ZXGmKBGJ3KusIoov/aV9TSi0+lYdTiLGf388XVtWvKrNVpcDQLUymbWViYVcJHLqKhX0xxfFwVF1S0vF+OWTMPVXo5MIpglBuzlUm4cGcpXe9NxkEsJdLcUX9+XXIKPix3R/i4s3ZhIjiEs0lWR2oQc27TjrqC2QUtqcS3vbE7ki5stHBsLvjmQ2a7jikB+RT3R/i4cfnZai9utvGMEifnVTIo2L1Xbl1zM8+v0Wir2MoyTur2cFSy7wby64ovdqWSU1vHH8VyenGk+kPZi8eiMaB6dEd32hu0gxNORuCXTkAr672lWaR3Prk1gX0opggBbH53YrptnV9JtcpSiKBLgZs8zM5vuwFrATipham/TyQEdv6RbVxxhybrTzPxgj9nzC7+OZdc56xJ6ao1IjcqyMGtAoLPR+I7r5Uljgtv0qrycFChk1kcpPTglkgcm9+TrW4dZZHgPpJSw8OvDXPHpfuobtEiaZc8V7RhVbuPSwc1BbhyT1RbDmhX8t8Yfx3O59vODHM+usHjtvh/iGfLqVkprGpjc29eiAsPTJNY7rpcvPs76x9VKDb18zI3Nc3P7cs3QYG4aaT5tppHY9DKu/uwAfydYbxy5EMprGhj95nau/vxAlw6IlUubvqe/H8thX0opUkHA39XeapNHd9NtBviL3anc/HUsb2421+ocGu7J17eO4PCzU9n71GRjfW9HcDEss5vHgFsTY9eh97hNifB2JCGvqetuX0oZH14/mJtHhZktH1sLm7s5yHlyZm+ryxtfVzucFFKCPRwQRdHig63WishtRvgfw8QoH45klDF32V5O57Vef77shqF8f/twPBza1hd4e1MSsRll/H40x+K1g2mllNU2cNNXhxjy6hae+O24mQHrG+TGy5f1BWBbYhE3jwoH9J2aj/xsXpI4va8f71wzkEB3B6vX8VtcNnGZ5Ty46hjRSzai1XZe9crTvyeQX6kkLqOcfi9sMnaUdiXXDgth/sBA3rt2IPufntLmxJzuoNsM8MpDlkuw2w0j5QH8XO2NcnEd5ZObhvLzXaPY/rh5+dnXtw5n1Z0j+fGOEdjL236reVYSb/UNGpbMNReLb0vovSV6+boQ//x0Nj4ygTXHcslultUVwVhnaePSJ6+yjq/3ZXA6r4rt7VCcmxDly72TeuLYxmfRUSHlmqHB3DW+h8Vr398+AoBKpYayWjWr43O587tYTmQ15VauGx7KXeMjeO3y/maSr5tPF/LEbyeY+u4u0kza+5uTVlxDXkU9907qib2hykOl0ZFU0PI+7eGbfelEPbeRFfvTSSpoqnZQanRklXV9ojjQ3YGPbhjMgsFBl0xNcbcZ4KFhHgiAs0LCvBh/0t6YzQvzO2cGHMConl44Kiw93jG9vBkb6cPGRybg6dS69+FqLzMrkPd1sePpNSe5e2U8zia7tjReqT3Yy6VIJQLZpXW09D30NEzS7eVzcXvybbROQmYFAOMjvVk02voyvjn3TOzJ5zcPw9FKxU4jtQ1a3rlmoIVDUlStZMnakxbb70wqYcGnBzhj8MLt5VKem9uXhaPCcLKX42X43DdoRdYezSG1uNYYXpj0zk6Kqpocj3OF1cx4fw8z3t+Dl5OCp0xiwxHnsTo15Vh2BQ1aHceyK8hsZnBn9++YPOa/hW4zwMtuGELSa7M59cpsPr5pKJLziPVeCBHeThx9fgYbHm65N35Kb19eu7w/jnIJz83ug7chfnYsq5way1zdebM3uZgv96ZZ1P8Gu8kYFuZujE3NiQkw85YifZxo+WsLDvJL467+X6FB1Hf53TuxZ4fqbN/ZnESduuPL+Q+3nSMht+WkanW99WZzUwmA60eE8uyc3kyM9iEus5yM0jq2nGkKhTUK8ihkEvanlHA4vSmHklVW2+FrNuWl+X15/Yr+vDi/HxHe5jeXtsS5OgOtTmTRN7HMeH+32U2nLQa/soXwxet5b0vnjxLr1iRcW90oB1JK2H62sEuvo2+gm9lkDFMC3B24aVQYZ16dzV0Te/DT3aN5cHIvxvW6sHIVtVZn/Ie/symRRV/HGl9zNOmiyqnUEJdZQbKhbXfZjhTqTKx0cnEtrX1t6ztZ8NtG26QW17Jw+WFUmvYbVI2hNz3QzbJKppEZ7+8mpch8yX/F4OAWt3dWSBjZ03oj0hc3D+PeCT14cmYU82MCCPV0QGGyilvyx1nj7z18nNn71GSen9uHB1Yd40BaGSMiPLlheAjR7RQdagkvZ/1EHE8nBTufmEywu77u/9qhQRRVKXn4p2P8ciTrgs7RGtVKNfuSizlXWENSB+bhlRtUGa3pbVwo3VKGVt+gZe6yvVTWq/n74XEEuFkG/fMr61n49WF0Ivz90Dj6B7nxy5EsXlh3miGh7vx09+hOvaZQD3uyypvugg4yiUXLppuDnCdmRrPtTCEbDH/8vn7WExbNOZ1XyR0r4hjTy4uTOZUkF9Ugk0CzKTLUaWxG85+OiD5x9vy8vu3aPrNUf4PNtzLAtZFzhTWsPJjBNcNC6B+kb67wcrZjXowff580d1IUUgmrW9Ek9nGxY/GcPmw6VcD1yw9bvN7cL/J1tSfSzwUHuZQIbyd+vadzv3uN/P7AOJILaxjby5uv9qTx54k8diUVdZnambujgi9vHkZRtapDTtUDk3uy+VQhK24b0enX1C0GuLZBQ3Z5HWqtSFGVyqoBdnOQ09PHmVqVBn83e/anlBg7eQ43m//UGTw2LYr//ZZgfFyv0fHSn6dZetUAi21H9PCkl68TpTUNLLuxfUMMT2RXUlClZNuZQqMIe3Pja+Pfgyi2/5/r4aSgrkLZZu33dwcz+e5gJnNi/Nl4sqDF7dU6nXECc2sUVZknfV+aG0ViYR1vNvvMr9ifxkt/nUUQ4OtFQ9s8rjXWJ+Ty1Z40Vt45CheTyRJltQ2cyaukRqVlSm9fxhoM4fyBgRzNKjc+7iqmWZnZ2BZPzuzdZXXS3daKfCyrnBqVhvGRrav0P7AqnvUJTa6+TAJPz+rDXRMsM8IXyrBXNlJS1/TB9XKWE79khvFxZmktznYy8iuVZJXVMbOvH9JWEnBqjY47VhzmZG4Vap1IkIcDz8zqw60rjrS4T+PkXBv/fM68MtNqIriRBo2O/62KZ8OZzp3Rd+uYcF66rH0J7as+3U9KcQ1vXTWAWf0tB8Rml9Ux/u2dxse/3D2SkT06bhRNW4pX3jHC+L2Pem6DUTvloSm9eLyTmi4udS56K3J7R5xsSDCPs2h0UFHfcldaR3lq9QlWx+dYnVRbWqPmju+O4OEgJ7einsPpZbjYyag0GSMU7efMugfHseVMIXkV9dw1vgdSicBDq47yV0K+2fGSCmroF+TKseensS+5hCg/V8J9HPlg6znkUgkLR4WRVlLLgZRSfj6SRVH1hYsFeTjKjTGrzqSjanL/RX44lMm643m8dnl/q5/3lKKaTje+AN8fzGi3AW5rfJKTnRR3Bzk1SjVzYvzbZXxVDRrGvbMLrU7HtkcnsuWMeYjk5q9j+eLmofT2dzETrvpqT1qnGeBqpZqv9qQhl0ro4ePM3AH/jOnjl5xQgDVf8LsDGcSml/H8vL4MCHZvdf+9iUUEeDjQq4Xe9l/jLIvbTTGt5xTAzPgCJBXW8MnOFD7emYIoQpSfM1N6+7HrnPUv1sncSqb09qO8Xs0nu1J4cX5fnprVm7nL9vHZ7lQ+uXEISYWVnWJ8gS4xvmAzvu1h5aFMssvq2XSqwKoB7hPggoudhGpV5/41m3dUtkW1Us0Vn+o70P64fyxujk0hgpUHs4zt+XXtVLH7Zn8GxYbP703LD3HWSr2wl5OCQDcHM80TpUaHTqfrlIqoVYezWLajSf8izGucMXZ+KdO9tWCt8P7WJFbHZfPy3KY74saHxjAp2ocalZYjGeUs3ZjY4v4VtUrCF6/n5hVHmPb+Hracybe63aCQ9v9TXr+iP+FWmkNG9fBi3oBAhoS6MyDYnfe2JFGttMyC3zwqzNgR99r6s/x5Io8NpwrQiZBRWotaK/LIz8fY2k6v6HwKdVwUAq3I1droRCqr67lzXAR3jLc+iUIUaXeiriMoZBL2JRe3e/vSmgZSi2tIL6mlsNo8Eejq0OSTmeq2NMc0dHnFkKZ2bGvGF+Dqzw+y8lCGhYN1PuVnCTkVfL0vHaVJKd/4SB+i/Jzxcbajl68zwR7tS5ZfbC4JD/ibfel8aBhD4uuiMIsZLb1yAM+tPUl6aS03jGg5O3pbszhrXEY5M/paLkP+eKCpDviHgxm4O8p4dX0iw8M98XSQ8f3hbCQCxC2ZjruDnPe26qcrL180BBd7BcPDPZBIJGbJgjCD4pmdTIK3s4JcQ0fdq5c3TVV9dnZv4jLLmd3fH6lE4K0rB/C/X44R6unIkDB3foq19MybV02cT6R411NT+d/Px9ibYn0aryktaRvbaB9Vanh6dm92JRXz/B/7uXN8BHeadLM9ufoEa47mGh8LwOgID1JL61A2aKi0chNvD3UNWu78Lo4ePk58ctMQIrz1DRMZJbW8tv4sc2L8uXJIUwlbuLcT39w6HJ1ORCYReHp1AtcOD2ZomCe3jY1gxf4MssvqeGJGlNXzmcpqPju7N28YHKO2Pj9f7kkzezwh0uu8DPB9Pxwlt6IeURSNf9++ga5seXRiG3teelwSHnDfAFcE9B/IouoG9iaXsPCrQ/R8Zj0bE3LwdrFDANYdzSF88Xp6LF5P+OL1LF5zHICKugazYZdRvk48O6dtT2Ph6HCkEimFVSrWn8znvsmRRPo6s2BQIJ5OCiQSAU8nBTIJOMhlFFQprS7xrxoawuFnp3Lb2HAEQcDLSWEh0nLr2Ag+vnGIcezMxtP5aEWY2sePN68cSMbSuRb1yRdaNbHqjhF4OdsxsI2wTSMXanxlEoE9T03mNZMbz3+NyOc28tmuFAqqlGw1iYVuPJXPH8f1xlcm0TdwnH1lJifzqimsUlGp1PLUTOvxUMe25SNQanScya/m1m+OcO3nB0nIqWB1fA7bzhbywTa9/kpdnZr7foinoLKOydG+TO3jx2e7UvklLpu3NumbDJZuOEtmWR06YFY/63FU04/lT7FNdbsNWpFHp7WsN6xppiVxx7jzS6xP7u2Dn6udmVB9R6hr0FBZp0aj1XEyp7JLxYDa4pLwgEf19CLptdmkFFXxc1w23+5PN2oivLw+yRg3SjU0KDR+AH4+ksvSqwbh5iAnzNOBzLJ6Irwc2dyBO+GwcA/8XO0I83Tkmd9PklxUQ3JRDa8t6I+TvZxVd45k2OvbucnQPCEVIPVNy0YOP1d7/k7IJ6e8nidnRvPA5F6tnrcxW+5sL6NaqcbFXk5KYTXOCgk1DZ0TI7zx61huGxPGt+2UQrxQNDqR3eeKuXZYMEXVSrPhiv8ljmZVYC+X8LphAsOjvxxj7bGmbrObRobx8oL+ZJTUUm1Q5JNLBRaODmPrmUKOZVdw3bBg4jLKyatU8sJlfVl1KLPVLjhPRzlldWryKuvJLKvj96O53DEugozSWmYZ2nz7vrIF0DcU7HpiEj/FZjG2lxcZpbUMD/fg8V9PsCux6aZRpbR0NqqVaqTolQxDPRx468oYrv2qqbY42LPlpX9JrXk+Jav8/PQfXrs8htcu7/h0C7VWx8S3dpDXTPP7uqFBvHXNoPO6lgvlkvCAQR/H6hvoTm6Z0kKQpneAC49MjcTfxbzdc3pffZhCEAR2PzUFTyc56aV1LPh4f7vqIgE+351GYZWK2IxyxkbqO4nsZBKjkPm2Zp15rTmJ7183iEemRnKziS6AsoWW05cv68eoHp68vSmJQS9v5VBaKXM/2tdpxreR7jK+0/v6MqOvHwdSirnik/0MD7c+5++/greznG1nCwlfvN7M+L5z9QBjxYIg6FcNCqnA/sVTcLWXs/aBsZx+eSZXDg2mXq2lXq0lo6QOL+eWW52vGhLE5wv19bpqrciQUHfuGBdBiKcjH984hHkDAi32eXtzIl/sSWPDyQJ+u3cMOxOLWXM0h1KTFZ6/lU49QRBwdZQjlwp8sWgYumaZiVf+OtPuXMXzf5xmzBtbOZFVAejjyjsTi8ir6Pjoofbw1Z40C+ML8Et8rrGTUanWUtiBNuUL5ZLwgBspr23Ax8UOe5mEBq3OWCp27bAQbhsbwaPTrcekGqkyfHgSciu5e2WcsXMlvaSWhJwKxvT0Ys3RXGb09aOHQVjk+hEhrInXhzluHRPBtcNCcZBLjbEpO5m5+kKIZ8vto8PDPY2Gp1alYcRrW6hVi7g5yNi/eKqZPOY7m5M4lKZvMNGKIoVVSuxkElT/wG4NiQCf3jSU34/mGJtnbjZpt/4vklOuskgab3pkHL0DmpLAGaV1aHR6CVInk/rhU7mVXPflIQDG9PRidE9Pnp4VTcQzG6ye68EpvQh2dyDYw4HKejUfXDfIqrLgx9cP4MGfEwhwljFvQCBJBdXMH6g3zneOC+cxk8YkB5lgnEhhyk+Hs+jt78rjM6L4Yncqfxw3l1StbEGPoiXyqhq48rP9/PXQeI5nl/Ps2lNEeDux84lJHTpOa/xxLJcX1p3CvRUp0E93pPChSRXF+9cO5IohLbd+dxaXlAG+9ouDJBfV4GIvQ2liiE7ntW+qg2lXr+m01Vu/jSWztI4R4Z7EZpSxM7GIXwztlZG+Lhx/san5ws3BfFFwxZBgnl2bYNRZ2PtU++ZS/XIki1rDPpX1GlbHZXPrWH12/M8Teaw4kKHXxhBF/Nzsmd0/gKm9/UgvqeHn2Ex+tJKUkwBSqXDJSVbqRH3CZ1i4J45y6XkJzfzb+ej6AWbGFyDCywkfFztCPR3NtKxrG5qM2KncCm755gjLFw0j+fXZRD630eLYk/9vNzeMCGXf01NavYZ5g0KYNyjE+HhOTFOM94ohwWYGuF4jciK7nIEh5nHWT3alUFGnH4JbaWWazPmgFWHOsr1M7+uHTCK0KUt79Wf7OZpVwdieXqy8c1Sbxz+UVkqVUmPsSG2Ov4uCv06Y30ge/fUE/YPcunxU00UPQexLKWb2B3v4+0QeboY7VKSvs1GYfHSEZ4enmUoEzKQu+we6oZBJGNvLC29nO6sTmFvj7KtzSHl9Nu9dO5Bz7RTxGN2sgH1CVFMHoKejAqlEoKePE1oRiqtV1DVocLaXIZdJrBpf0Me+H5jUiwWDAundyhy8i8GsD/ai0YqcedVygOSlwqz+HW9D7QwSX5nFfBPD10iolyNHnpvGmvvGmAlVTentx1iDsI6DXIZEgF/istDqRMZHWg/tXOiyWRAEFM1iBws+OWCx3WPToxgS4s64np44KzrXfAS42XPshemsuLXldv/wxeuJy6xAJ8LelFI2tUMg56lZvVkyt0+L8q/bHp9ktXQwveTC1N/aw0XzgL/dn86K/RlGXdCHfz7GmVdmkVpcY5zsqtHokLUx9tuUhSND+fVIFs81q7X85KYhaHV6NbZHprUexmiJn49ks+SPU/i4KDjyXNuTcPsEurLxkfEcyypnSm8/s3jauEhvjjw3DWc7GbvPFePppDDKGbY2EWRCpDcKmYR1x/Owl0sYHu7BEZMhp0/P6s1bm1qule5KZFLQiTpyK1pPrPg4ySiu7dgytbMQLtLC4fovD/DDHaNwbsc0jEYax93P7O/P9wcz2XqmiIOppczuH0SNUsux7KYJEsEeDlw/wtLAt0RSQTW1Kg1DmlUR/Hb/GAujezyrjEGheqOfXVbLi+tOIwKzlu1v9/nay+WDAnGxl1NcreL+H+MJ9XTi/64ZwNNrEvjjWC63jbHUXH7s1+PYy4cwKbplp6qsVsXnu1Mt5F8b6f+SPjkpE2BwmAd5FfX09ndlRr+u1yju1rH0ADUqDS/8cYrfj+VavHbs+emXxJgQpVprtiTckVjIC+tOkWNQT2tJztKUb/encTy7kvoGNUXVKirrtQwKcef96wa1ut+ZvCrmLNvb4uuXDQgkuaiaohoVpTWd16J9oex8YiJzPtxHvVpLhJcj6aVdP+GgI7jZSxkc6smuc+1vWOhMBCAmyI33rx/UrrFbacXVlNao+GRnKrvOlSAAGx4Zz8Llhymttfy/O8pArYPkN1r/bBZUKhm9dDuiCEuvjOH6ZrX1M97bxbmiJs/PQS7hu9uG83NsFr8ft97c1NncOCKEVbHZAPT2dyGxwHLVOTnam30ppai1IqN7evHTXS2HIpZuOMPne9Lbff5rhgbzzjUDO37hrXDRtSAa2ZdcbGZ8nRVSagxTkbecyue6FgYEdhcPrjrKxlMFfLFwKNP6+qFSa7nru7hWqx+acyyrnJf/OmvxfHZZLZOivHlrUxLFNSocFVJ2PTGZ21bEIooikb6uJBW0Hu/+swsGJHYGEd7OKA2Z5PpWYsASAas6HF2JFKhUai+a8QV9GWVCbiVT393N4BB37hgfQZC7Pf0D3ZCbJHp1OpHFvyfwa1wOCqmERaPD2HWuBBH937WXrzOlVtQB6wyLivDF6606CGuP5fDLkWyuHRZinGlYYaWmfelVA7n2iwPGGvR6tY5rv7SUsAT9/3LNvaO54rODHfpbtMWq2Gxc7WVUKTUWxtdZIeXKocHsPleMWisiANcNa937b6+a4rienuxLLcOzG53AbjfA4yN9uGZoMK4Ocr47kGEcSQ8wsZVlxPmQUlRDlVLNtjOFzOrv36aOROM+Wp3Io78ex8NRwaQoHwvju2x7Mq72Mn6Lz+HVy/szpFnff0Gl0qznvRFRhEd+OWF8XFmvYfCrW42PTzQbIW8vF/BytCPXoBv7+PQo3jV05l1qxGeW8fNdo7j9uyMUWCn1aeRi1LxfainBY9kVPLjqmPGxnVTAyU5Gg1qLUqsj1FOfQG7Q6lgyry9XDgmirkHLkFAPll41gLnL9lLX0LF39c2+DE7mVpqNQjqVV0ldg8ZMwU1AbHcDkE6Eh38+3qHrsIZpKE0q0U8yvnpoMPtSSkgvqUWn01cKAbg7ynhlQX9u+SaW3PJ6vrx5KFP6tB7bf/fqGKa8v6/N69iXWsb4SC8Wz+4a6UlrdLsBdrKT8c41AymrbWDtsVx0oshH1w8myt8FP1d7fo3LxlkhY04rakalNSocFTIcFC0P6MkoqWXG+7uNX/i9ySWsvX8M3x3MpIePE5MNxr6stgGdToe3iz5G+/Wtw7n7+yOczqumWqkhLtPy7vne1nOEeTqSWVbHC3+c4u+Hx5u9/tJfpy2Mr0TQ1xe3JnBy65hw5BKB7YmFpJXUsWRuP4aFe3D5x/vxcrbj3kk92XWukPjMSqJ9nRgc6sm+lGJyrAwT7W5i08oYH+WDViviIJdQ304hFxug0oqoTLzRNEPyZ8kcfWdc38Cm6okIbycentqLL3anWe3K/O0e66Lhz8zuzdpjuTwwuSdFNUc5lVvN3wn5zB8QyExDo0bU4vW0N6jV2CZfUKXExU5Ktcr8huBsJ6HGRHTo5fl92XWuiJ1JJWbbfXXTUKbHtB1rHbt0O7kVSvIqVeh0It/eOpx6tRYnk9LO2PRSvj+Ywa7EIsb08uHLRcOob9Dyxd5MQjwcLIbgWuNoVkW3jEdq5KIl4TydFOx9ajIiTePjfzuSzVNr9KUw673G0i/I3WK/hJwKrv7sIIHu9mx/fFKLY47s5BKz8fG19Q3EvLTJWE42KsKDwiqVMVY5LMyD1feNIcjdgfLapg/2q5f1x95OytxlTXdQhVTAz9WezLI6TuVVWXgRVw0JZsPJfDQ6kRzDP/3F+f0sEmT/d2UMT/yur5v988GRDAjWV048NiOazLJaevu7cjitFKVGR3ldAw0aHcPDvYnPrGRSHz+emd2H5/84xcpDmQwL9yDOJCHX3by1OYm3Nnf+zKx/EtZWPdZeD/ewI6O8bfW71zYkkVVWzyvNur7cHBQtqt5d80Ws1RDEmF7ehHg4UlSj4lRu07L+r4RcZvb355nVx9ttfEE/YXh8pA9zBwRQVdvAvSYePWBmfAFe/OuM1eP4urdcV29KD29nciuU9At0M040NjW+ADd+ddiorb03WS8ktPJQBr/EZbfrHABPd7M+8UWtA27+Byyva/oING+AaKRGpaFBq6OyXm2sbDBlwUd7OWGlZTOtzPzudyjd3FiZzt8a28ubvxPyeWVBP3zd7C3qEhu0IrEZZUyM9GZ2TICFCPdTs3rz1Cz9MuZoVjlZpXUsGBRIVlkdv8Vl8+ycPsbkx9VWBIYcFFJ6G+ZvjeyhTzB4OytwspPx9KxobhwRSoinA0XVSn45koVEgGuGBF9UA3ypMjTUjfgsfcWAm4OMqnoN8i4SHWrriCJw/bAgfo6zTEC3xPeHsrhtbITZROL5AwM5k1vJysPW56e9s+kMT84yrwR6f2sSH25Pwc1BZtbuPiXaj+LKOn5q4Zo8nRSUNUv6KaQCt4wO5w4ToaHfXO25/vODaNG369vJpW2GSVztZdQ3tK8i5stFwzhbUMXgEPcWt9GZeFz1apHssjr2t0OEypQX/jrDsB6e9G1Ws91VdHsVRGtotDre35pMiKc9149oORl3IrsCbxc7gtybOnUScip4anWC1YxpazgpBG4YEc71I0Lp5dv0IU8trmHm+3tQyCTsfWoym0/l8+wfp42vDwxx5+MbBuPtbMehtFJG9fBqNSTSFVTWqZn63i5Uah0f3TCIW1fo/zcOMoF626w5q7g7yHF1kJJVdnHCNgoJnE+3ualX26DRoZBJmPbublKKLeUf7aT6lue5A4ONDsqjvxxn7bFc5FKB64eHsvJQJm4Osg53rjkpJFw9LISs0jreuWagUVwK9J2sX+5NY3K0L4fTSnl36zkuGxhIQZWS6joVZwut19VueXQCUZ3Q8GA6hQOgX6ArT8/qzRd7UjtkiN+9egBXtZHY6ygtVUFc9EYMU2RSCU/Oiub6EWHcuzKe8W/t4LZvY1m4/LCZMMjAEHcz4wvwxvoz7Ta+mx9pitnKpTKuGhpsZnxBP+hQLpVgJ5Mgk0gsJtK+fFk/QjwdeXHdKW5bcYQnfzvewXd74bg5ytn15GT2LZ7CpN5+zOzjS7CbfbuM74ReXsibrR6GhLp3SC/ZGu9eM5DJ0T4WY8e7C2kL4bvG3FOwu327jG9M4IVNALaGo0J6XsYXYM672wBYdzyXqCUbiXlpM4/NsK48ptLCw78kEP3sBk7n6b3/d68ZwJMzo1l7/1gmRfvgat9x4wt6edjvD2SyM6mY/Snm8VwPJwVPz+rNiAhPHpoaSfLrszlXWE1sepnR+L52WV+em22+zP9uf/tLxKyRVVqnb2SyM3eAxvfyxsVeRm47Yr+gL8vrG+DC46v1dcfdwSVlgBvRaHXsSCwiu7yenUnF7Esp4XQrSlCAWZthY8eLgHmMRS6B1NdnEx3Q9OWqqFfzq5UYUYinI3ufnszOJybh5ijHoVm45IpP9lNUrSQ2Q5+k23q2bVH17LI6forNoq6dy6724Gwnw81BjiiKfHTTUEJNWrBB73GZ0svXiSsHB7InpRS1SUmCp6OM3+8fy6c3DeXmUec3lVYQ4O+EPL69bYRFadAto7tm0m1zQltQ42qsjEsubvLCeni3rNz14NTwTrmeZ2ZEGAXxly8ahr0VdfyAVsbTNzKhh76T8lCa3pOrVmr4fGfrFTEa4K2NZ7l3ZTzrTxbwwORe9A9yI6e83mpb7gtzo/nj/jGtHvOhn48jAgOC3JjRt/XkWWJ+NQHu9kzv21TdtOTPMyzdZH7dP8Zmk5hf1e4uU1M2ny5gwjs7ueHLQyS8OJMvbh7K/sVTyFg6l28PZHDFpwfIaEdN+rQ+3lw/ItQYTiyp6ZwJNW1xSWlBNCKTSlhx23BSi2uQSAQq69WMjGhdXevdawYy25AoC/JwJNDdgQaNDplU4MtFw3A1mcxaXqtCKuh70BvjWdYwXV4B3DepB5/t0otKi+i95B4+zmSU1uFi3/af8pGfj3E0q4LssjpjjLizuOLTAyQVVJsJjng7yVl24xDyyut4f1syI8I9ef/6wWSV1iKVSAj2cGDloUy8nRWsMwjVB7o7UK3UMD7Si73J7V+2Ocr0taiNmeZbR4eydFNTUu7XuBx6+jiSWtz2l+FC5s+ll7bu7cgkAirgtcv7804rXYP3rEwwe+woB08nO3IqOvbF/GBbOpsfm0BVvYbfj+agtLI6aW08fSNb08rx25/OT7FNzkJCXtutsnsM/8OkwmomRfsgkwjMGxDAi3+etth2cJgXzxiSwm0xb2BAmyG3tzcnsje5BJ9mSm5aK2HPucv2ohXhpfl9ifBxZqJJ635rHEzVe+F1Ki0SicBMk+61jghbPW7QYv5q0TDO5FcxuodXu/e9EC5JAwz6rO2YDoyo7hPoxh3jIvjrRB4vX9aPnr7OjHtLP901qaDaTB7xpb/OGGt7E1+dbcyqtsVlA4P4cncaOmB0Dy/cHRV8efNQdp0rbvMGATA0zIPTeVUMbCWRcD7odCKpxTVG+cJG5DIpN351GAng42LHq1fEsDOxiDu+O8KYnt68c81Ai9bsW785zK5zJXSUOg04KiQ8Oi0SjVaHvZ2CjKVzWROfzbNrT1Gv1hHl60J+parN5ExLXxsB+PWeUVzzxaEOXx+Aj7OCb28bQVJBNYFu9sZ5fzeMCOHNKwdQXKVi+BvbrL8/NTidR1y9Xge3fRNLVvmFxZxTi2s5mVPZ9oYt4OEgZ9DLW9CKLS97r/j0AP6urXvj/QNc+PimoYQ3W2lZ44YRoVQrNYR7ORpV0766eSin86uMIvGNNH4fX/1b/93868FxxAS3Hg47mVPJCoPcqulYpEYWz4pme2IRX988hNu+iyfeIHtpjeu/OMyJF2fi4aQwm3bT1VyyBvh8eH5eX7OZW29fNYAqpZphzXreLxsUyIaT+YR5Obbb+AL0CXBl3+IpOMplxkGGMqmEaW0Ugjfy3Ny+PDe382eCSSQCv983hl+PZPHVvgzj841qVTqgsFrF478eY2KULzoR8istPcUGjY7Dae3rGnJSSHl4cg/e3Nz0Rapr0PG/X44T4e3EhofHcyitjKfXnKRfoCujenqxcGQYT+t0LPo6lqxmcblIXycemBxJfkU9721JQkfTl9LdQUpFvRYRiE0vZ0S4B7HnUfFRXNOAi72MK4cE8cK600T6OnPzqDAWjQkHYMVB67HIW0eH4eIgZ1QPL3YmFrLc5G/cHi7U+AK8sqAf1wwNISbYjV4+Ttz34zFqVBrun9STyVE+XPNl6zelo9kVxt+b3+Dmxfiz6VQBGhHcHeWMj/Tmt3jrglCR/q7tMr4ZJbUcSivl1QX9iQl2445xPaiob2B8pA/T+/mz6lA6RTWWYZDG/7mvqx33fBfLjqRidjw+kSAPJ25cfoj0klp+vWc0LvZyVhzIAEAqCEzrY9nEde+kXtw7ST8YYc39Y4lNK+XaFv5OSit16zqdDo2uSZejK/hXGeDmXDvceiZzam8/kl+fc17HDLCikXopsPj3k8Rnmhul5p7m5tNFzBsQxAfXDsLDyVIYRkTEXiGlvh1Lt9oGrZnxhabqi5zyetRakZxyvd7tiZxKrh4aTIinI8XVKgvj+8F1A1nyx2leW3+WBYMCUIv6Urz6Bi0PTO7JHeN6cNNXh2jQaOnp68S7W8+v3E4mEXBzkFNRp2blIb3nFGQY3phfWc9xg5GykwpcNTQYnSgyKcqXWSayjWN7ebNiXwZdJSfk7SChpN787+/lKGORIUx2m0HSNPa5qeSU1xPu5UjUkk0XdM6/TzYpiqkaNNwwMpQjGWWU1TZQpdQQ4GZHfqUKAb02d3tYtiOZ34/mklpcw493jrLwZv96eAIj39gB6JOTzT+rI9/Ybvx9/Du7zV47kV3JobRS1hzNIdrPha8WDSPUq+2k74geXiwcGcoPzcr3HORS7ptkPh6ppEbJqDd2oBVF/nqw6yYsd3sSrrBKSfJ5BNttmCOKIveujGfWB3vILa8jqVkFiLWbdqCbHb39XXjxr9Pc8u0RYtPL0OlEnlp9gtu+jUWl0bHpfxPY8Mg4y53bwYz+/vx450j+eGAsDgop1w0PoX+QPuFZYhAO8nGxs4ivHc+uoK5BQ7VSTY5BHa++QcsjUyN5ZGoUnk4KNv5vAjeNCufeH46e17WBfmSSRqdDKhF4aX5f7pnQwzj89ZavY9mfUkpPHycOPzeNDacK+PlIDinFNexLLmaFSab++flNWXwB2PPUJDKWzuXkSzOanxKAp2b04NAzU3l8esvz0hppbnwBSus0NC8XdVTIiPJzYcvpQovtL4ThEZ4MCfXgpcv6GRN1g0M8cLWXcfWwYEb3bF9s9MrBwcQEubVosP1cHXh4qt47rWvQ8tyM8HZf43O/H2eVYRbdtL6+Zsb31b/PMPGdnS2Ga6ypIdartRZlcEVVKjQ6EVE07xHobLrVA1aqtcz6YA8V9WrW3DfGQkPBRvupV2vZerYQrU5kV1IxNSpzn8xRoRcz8XJSMDHah3evGYggCKi1OlwdZNQ3aHGyk1JSq+LXOP1y83hWBROifPBrIw5oDYVU4K7xPc08BUEQWHXXKE7mVDLKxOiO6+XJwbSmBN+p3CpW3Dacl/86Q1qJvmnFzUHOg1N6IZfq7yT67L/eCF2InsTrf51h7Yl83BzkxC+Zhsxw/MafHo4Ktp0tNArVpBTV8H9b9Fn77PI6np/Xjxf/akouisD2s0XcMCKUVS00Rozq6YW/mz2XDw7m3a3mqwZvJznl9Rp9U5HQ8sirerXWmKHX6URj6GxGP+vhLzupvhyto/x+NJdh4Z7MHRDI9L5+SAX4+MbBSCQd89XGRXozLrL1G/ld43qw5XQhGq2OAC8Pnpqp4O3NbWudVDc0/ZH6N2uY2HAyn/xKJQfTSqzGkH1c7OgX6Go25MHDUW60RYVVSm748hCRvs68fkV/KurUXD7YMr7cWXSrARYEfYebVNCgkF6SFXDdwonsCoI8HCyqLDqCo0LGtD6+HEwtZf1JS4W0KqWGfoGurH94PEq1luV705gQ5UO0vyub/zeB+gYtXobzv7qgH8XVKsYYvJuq+o7JXAZ7OLQ4jcHVXm6R1DDVsgWIyyynsl5jHLr60Y2DjZ2AAPf+EM+mUwV4OSmwkwqoLqCLrcpwo6pWqlFpdEbD+93tI9h6ppA5Mf5sN5kDODzM3ZhAyquoJ3qJ5UQKO6nAsu3JfLor1WI0u5NcYEiYL5V1aia+vdNsv1ERHqy4fSSltSqS8qt5fcNZUotrrY6majS+j/1ynL8T8vnqlmFMjPJBIZOS+sYcdiYW4ufmQFlNAzHBrgx51XoysS00Ijy15iTvbE7i/sm9mBMTwPVfHWZmXz+zzreOUKPS4KSQWmgslNepadDoSCup5cGfjnHvBEtR9LaYEN1ULVGj0rDshkEcyShn4aiWG7nuntCDdzYnMX9gANcNCzWLaX+0PZm0klrSSmp599pBOLejuulC6FYDbCeTsuWxCdSqNJdsLLWr2XQqn3t/OEqIpwN7n2p9hIw1diYW8eKfp7l9bDjbzxah0YkcSLWeOEssqCKztJZHfznO0awKPtyewqmXZ+KokJm1T187PITHfz3B9Pd3kVFS32ZLbXNy2lno3sgbV8ZQuOIIJw213Q9P6cX8gYHYySRodaKZ8a1Wqok31FqX1TZ0+Nqak1xQRf9AN2QS/ZTcRnxc7LhxpL5WudFLlQiw5axewjLSx5mS6garpU1vbkritcv7I5VYtjjXqkUKKpVsOJlvkfw6k1uJvVyKBIFIfxf+eGAsvx7J5rrhoZzOr+TWbw5TrxZRmHSXnMytpEGrI6mgyliqJZUITOtrWn514fpvxTUNvLflHHvOFRObXkZcRpmFAVZrdcYVSktsO1PI3SvjmN7Xjy9uNm8E25tSbBQeAjqk2dtIo5zBrqQi7vgujolRPnzTykQNgAWDglgwKIj9KcU8uOoo71w9gD4GwaO7JvRge2IR4V5OXW584SIk4Vzt5WY1uZcC9Q1anl6TgIejnJcu69elakhOdvoRMy525/c32J5YSFZZHRtOFnDtsGB+ictB22xN7iiXoNGJNGhF3tqYyFFD+U3z70pZbQOeTgqSC2v4O6F7xLZBv2Q3lSFNM3i+phMI1Fod96yMZ1dSkTHk0BnN1VkVKjDU8saml1mdejA3xp+v9qajE/VNEnMHBHDjiFAc5BKubKZ9ayeT0MPbiQWDgpg/IJD1J/M5m1/Jp4Z6cYDJ/7eT7Y9PYtn2ZCpM5qhVNehIL65hyru7EYF1D4wxGrmREV6EezlztqDa7PP41aJhHMsutzrpuJH2TgRviVAPB4ZFeDIqwgtHhZT9KaWEeTmYhT4OpJZw6zdHmBjtw1eLLDpsjWSX16ETIdNKM8Tlg4L44VAWZ/OrGN3Dk4PtrMBpxN6kDDmnvB6tTiSjtH1jhFQaLQuXxyIC8z/ez6c3Dea+H44R5GHPwWea5j6W1Taw+1wR0/v6mw3V7Sz+1VUQ7eVYVjl/Goby3T2xp0Wbc2cyPtKHA4un4u54fgb44amReDrZMTcmgGh/F+YNDOS3uBwmR3nzsEFruE6tw9fFjqJqFRtOFeDhKMfDUcFXi4Yaj/P57lTj1N6rhwZxy+gwvjvY8gj7RaPD2HamgLxKvfGSS2hxxEtb/H40x2h0AcrrVBxILWFMz6ZQxa9x2exIbLu7sDltKZKBfq5ZbYOmxTE2z83ty7wBgexMKuK2MRHGkkOAH24fwdO/J+DnaserC2Lo6etsDKdJJALzBwYyf2Agvx/LoqBSH+4YHuFFoLsDx1+cgSiKZtON7/12v/F6m3en3TAylNfWnzUrsQr3dmqxDEyp1rIzsYgDqR2v424k2s+F3+8fYyaU9UtcNnuTS/h0VwoPTomkql7NxzuSadDqOJVbwdYzhfQLdCXQyvfmltHhhHg40i/IsrU7NqOMMT29OJtfdV4i6EotvPTnKV66rD83jgglwM2e3gGW57HGgZQSs8/Jkj9OoxVFskxEu6b+306qlBqKaxq4cWR5h2dTtgebAUaf+b1zXAQeToouNb6N+FtpPd14Mp8TOZU8PFWfeNqXUsLQMA+L1YKviz2PTW/K5I7p6c2Ynt7odKLRAANMivLh92O5aHQic2ICeL3ZhyevoumDdiK7kvGRPkT5OdMvwJXYjFJym3V8Te/rx5yYAG7++jBqrUiYlxNPzuxNZX0DI9rRhGLKi/P78eeJPOykAukldaw/lc/+1MO8fVUM1w7XhwF+NnR8tcegmnLt0EB+iW+KiUslgnGFsGBgIAtHhxmbcuobtFTWq43DYAE+3ZVCTnk9D0zqRbVSw6m8SrMY9rgoHz5bOJTLPt7PFZ8d4NAzU7F3kvLn8Vx2JRVz14Qe9AlwpV+AJwWVRSy9KoZrhjZVAmw9Y77SSCrTG933rh1orMgA/UDIlQczadDoSC5sXxZ+6cZEVhzIwM9VH9vv4+9CUqHeg26+SmqJpMJqHlp1lC8XDWN9Qj5PrUlAq9Pfadcdz2PPuSLiMyuMycIGjchd38cR4uHA3qenmHnJoL8pTetrmSj8OyGPB1cdw8tJwbHnp+OokLL+ZMfL6VYcyOTpWX1wUEiZ2s56fIBBJtOerxgUyJhe3jz22wl8DXmR4a9uodhElrZPFw3BtRlg9Ar8S+Z1foNER3jkl+M0aHSEe+mF3j/blcq0Pr4sv6X1eNYLa0/yvZXsu0Im4a2rYoj0c6F/oGU2+Nk5fYjwcuRoVgWL5/Rm1vt7qFZpOdfCl/3HQ5nEppehNnzzogNcjULeHaV/kJuxWqK4WsX6k3qjJDFZav9vWiRrjuZQU6dhTwc8OlPjC6DVifT0ceKnu0bha1LdoVRrmfbebspqG7hpVCh/HMvj+bl9eNvQPl1Zp2b9yXy2ny1k15OTzY7p4ajA1V6Gu6MCB7mUu747YtQCOZtfxYZHxvPFzUMpq20wOyfAb81kHxUSeGR6NFcOMRd7+t/PR0g2zGY7V1TD8r1p3NlGEizcUI41JyaAcb28eWZNgj580wHFQ4AdScX0fWETOhGjvu7EKG92W+mQbJxPV1yt4urPDpBYUM1v946mTxueaICbAwqZhAA3exo0OhwU0naPq7KTCbjay4kJdmNkxPmpEHo4KfjjgbFkltayYJC+yuEKk/+Bg50MDAb4kxsGMndgsNXjXCjdLkep1YmIomjMPtvQ88aGs8RllBHi6UhpTQP7Ukq4cWRom8ueqCUbaDC0yE6I9KS4Ws1Zk5rg/YsnE+RuvUh91eEsnv/jJApp2/KVMkGfIZcI+iqFGX3920zAtJfE/CrK6hrMQhDZZbXkVSp5+rcTZJR1LMnXnOfm9OGuCebGq1qpZtQb26lt0NI3wJUz+VXcOiYcZzsZ2eV1LBwVxmt/n2HegECLfUFvwKUSAblUQtRzG2kwSehdOyyYt6+2PtSxsq6Bga9sNXvO2jDa5tKK9lKBxDaahxo0Ol5ff5rDaWWkFNciimKHZhm2BwF9y3mtQdZNLtEbzUlRPuxPK0Wp1vH5wqHMasfNeV9yEQu/PoIAbHpkHO9sSeZIRhmOCgn5lS1rbjS2jnc1D62Kp5evE49Mu3DdlktiKGeNSsPsD/dQ36Blw8PjLbyD/zLPzunDwdRSbvhK3yq57oGxDGijFx7gjctjeGK1Xjhm6VWDmPveDrPXNS0Y1uTCaj7ecQ6tSLvkK2VSAYkoMLaXF3Nj9Amg7LI64jPLmRMTcEHtms3jdtV1Ksa/vQu4sDZQZzsZGx8Zh7Od3EK838Vezt8Pj6eqXo2zvYytZwq5bliImSFc92DLdayNU7PzK+uRywRMG7lMwzvN2d4srj2ltw+JBdX8FJvF/ZN7GitA7p8QwacmVQHKdljSdzYn8t1B89WQvUyCsoWk3GPTo/g7Ic+46mmPByoCtQ06vcqe0FT1sT1JXy3y4vy+7TK+gFlytbxezfJb9PYpv7KefcklPLk6wWKfmCBXXlnQn4q6Bk7n6UVz2isnIIpihxLsH904tO2NLpBuNcC1Kg35FUo0OpGSGsvl2X+dIWHu3DAiFDcHOQOC3dr1Ybl6WAhT+/ghleqXZeXNSngn/t8uevo6sf2xSWbPP/LzMWNCrT04KiRs/N8kyusa+PFwJlcPDebeH+I5nVdFbkU9D0zu1e5jtcWnu5sMj71MglqjY1QLWXJPBxllBl3bP+8fy5J1J7lnUgQ1Sh37U0r4NS6Hj3emMKNZGdSxrHLCPJ2IMCS0ek5se1S8Nd7amEitScdD3wBXPrphCDd8eYi4zDJevyLGrBtsTkwA3x/IILu8jrJaNUkFNby/NYnYjHLkUgnvXqv3nOOa1Up/fcuQNq/FWgKqJeMLcMWQQN4zGfLq42JHaU0DPi52xAS6GEvwrNGgA6lgaa1bagyxxoQoX75aNBSdTmRUj6bVT4CbA9cMC6Gwqp4vdqUS6uXElN5+zBsYSLQhFnv3ynhi08tYPLs3907s2ea5DqSUcNuKI8yJCeD96wa1+xq7mm41wH6u9vx672iUai19A115f+s5PtmZwsRoH75uI9b5X8BOJuXNKzueaTX12j68tj+P/HrK7PXUolpO5lQQYzIVuqAVL80aZXVa5n+0D4VMQk55PSsPZlJUpUQqcN7TDERR5P+2JFGj1LBkXl/kUgm7kopYFauvxujt78Lq+8ZQXttAiKcjl3+yn5TCGmb08+Otqwcgl0rIKq1j5ge7cVRIWXUki4TcKn6OzaWnjzN/nsjH39UeUdRPqm7k010pvL0pCTuZhKTXZp/XtTcyb0AAf57II8zLiWAPB243JHOPZJSh0Yn8fjTHaIBTCqvRiiJ/PDiOuIwyrv78IBV1DSye3Rs7uZSbTHSYvQ3/05ggV/56aLzVczfnqiHB6HQiX+1NazGWb0qIhxM9fZz0k4dFfRx3Rj9/np3Th2B3B6pVGtwc5Mx8fzdJVo5nzSm/8YsD7H56WruuF2B6K5rCCwYG839bkjmdX81No8KNxhfAx5Asa2xm0ul0nMqranHy+dmCalQanVHv41Kh25Nwpu3H3+5PR6MT2Xke5UY2rPPS301tsk5yCbWGWrEz+dVmBrjsPKYhFFWrmBTlTWW9mqSCakTgm1uHMaV3+70eU7LK6vhkZyqg9wxH9vDi74R8Kus1hHs7su7BsdjJpMb6yz8eGGtxjCplA/VqHfVqHZtO5eMolzCqhxfjI705k1fFNcOC8XW1p5/JhIsaQ7mXptl6u0alwV4m6VB+Ylpff5Jfn2Mxm/DF+f1YdzzXGAuOSy/laoOM5g93jGBcpA9r7huDh6OcHj7OzB9oXtfrZGiUMY2Lt4drhoWgkEp45Jfj7dp+++OTuOv7OLaeKUQnwqZTBUyK8uF6w0oM9I0zL647zf2TezInJpBPdyTx9pYUq8crqO5YF2VryKQY27N7NCu9W3bDYF6c39e4il7wyX5O5lYxpqcXq+4aZbatUq2lsq6B28aEs3B0yx1yF4OLWgXx9MxoXtuQyOWDWy4q/7ey8KuDHM+pZPdTk/By6pxQTH5Fvdm03EbjOzfGH39XOwa/soVRPbz4bOFQvJ0VRoGc9tA4hjy/SsXJl2by14k8CquUTIqyXkvbHkI9HRnVw5O8CiUhhkkWR9L1YYaCSmWLg1lNqaxrupFU1GvM5ov9eu9oqpRqVuzPwNVeZvSWnprVm0hfF7Pa1GNZ5Vz35SGi/Vz466HWNQxEUeR4dgV9Alyxl0utTua+eXQYKUXVPLf2JB9cN8js/1JcrQ/9DG0mk2qKTNaoUdHxpqBpff2wl0usSiya0pjo+/2+0Yzr5U15nYrkwlqmNysbe2PDWU7lVXH/j8c48own90+JZv6gYKb8326zqSoA8Uva7/22RmJBFZd9vJ8gDwfW3j8GL2fz74hUIpiFMCsNf99SK5/pP0/ksWxHCgqphBfmX9xqp+ZcVAN80+hwbmphGsW/ma2nC9lnaB8e+up25g8M4KMb2o7xtUWAuwNOCqlZlxnA+pMFZJbVUV6nZvNpvfRga6LoEV72lNRoqDYR+Al0dyCrrJ6REXqj0dxja+RUbiXXfnEQbycFe6zoQzz6y3EGBLly27geCILA0awKGjQ6diQW08ffhUyDGpqpVkh+ZT3L96Yzb0AAg5sJOOU10zUOayZL+M2+dD7Ylswfx3LZ8cQk4/PNBbyLqlU0aHTkVtS3maz5aEcK7209x6x+/nx+s2WiplqpJrO01tjYcji9jDkxAbxyWV80OvNyp5Z4dUE/bhkTRrSfC9nldbg7yHFpZwepk52M3+4Zw2vrz3A4ve3usru/P0Lc8zNbfN3bZKLF5Z/tZ//iqYR4OpH8xhyLao3+L23lysGBvHfdYBILqgj3cjImLDvC3yfyadDoyC6rx6kdXaN/PDCOn+OyuLmZBsSy7cmsPZZLL18nRvfw7tIu1/PhH1UL1lzx65+Kn6t5ydFfJ/IpbMdYGmtodSK3fhvL9Pd2882+NKRSARc7KTObJUOifJwJ83I06sl+eN0g/F3tuWtcuNl2903qQV6lysz4AsYOoc2nClm4vGXx779O5FLXoCWrvJ6yZt7IHStiWXssl5f/Pku+IQb93Jw+jIjwpE+AC/evapKa/OnupmXk57tS+XpfOi/9dcbifD4u5oJGBc3+juMjfQj3cmTegABaY2Y/g5Tm/WPb/JLaG4YONv5szrVfHGLeR/tZNCqMeyf2ZKqhk23RmAhuH9c+wRmZVEJvf1f+Sshj/Fs7GfLqVrMW48zS2lZlEvsFuhrnFbZFSa0Gjda6tzzurR1sPt0UImzuHcc+N7X5LqxPyOfL3anM+mAvD/zYsnyoskHDo78ctzqTcUKUfnxSv0DXdhlwT2cF90/qZXGTWnM0h/SSWuYPCOLVy/u3eZzu5h/TiDHl/3aRVlLLA5N68mQnz1PrbgaEePD9bcP47kAmR7MrCHBzwK+FwYwFlUo+25XC7JgARvXw4khGGfGZ5dw6Jhx7uZRfj2Sxy1AC9MrfZ437Hcko550r+/Pk7/qEnJ+7Pbuvb2oomN7Pn+n9/CmqUppN0SitbkDVSllaYbWKwmoV287kM62vpVF7dFoUZ/KrifZ3wbPZLLBgk4GZDnK9kfNyVhCbXsad38Vx78SevLkxEWc7Kf1MmkfmxASwN7mYwiold6w4wv7UEpbM7cvCUWFm7a9SAYva5KFhHhaNFC3R3lE0d0/oydQ+foR6Wq+vbqytH9PLi1n9Wzf8rVFZp+bhn44DoNaK1CpVKJwdKKpWMvODPWi0IlsenUAPH8sKjrK6hnb3X9w+NrzFuHdjSZ1EgEPPTLWoXPJ1sWfXE5OoVWmY+5F+JqOns4I3DG3uulYu4vk/T7P2WC5rj+WaVYpsOlXAGxvO8sK8Ptw0Krx9b6IF3rl6IHvOFXPrmAs7TlfxjzHAuYYPQkLu+c/FupSYEO3HhOi2k1fL96bx3cFMYjPK2fjIeO5dGU9pbQP2MgkDg915Zq3ewHo4yKhRaYnwcSK5sAaNVschk+XnZ7vSWDQ63EKFrqyuyUud1c+fX1sYRdOch1YdJcTLmYemRJqFI+wVMlbeMdLqPi9fFsPMvv6EeDrgboh7B7k7YCeT0NPHmXsm9uQeKyVFI3t44e1sR2xGOYWVSkQgLqOMhaPCiPB24qohwVQr1Tw7p49VPYKuoKeJ0dt0qoD/25zEqwv6MbqXN7/eO5rCSiWRHagO2X2umNj0Uu6b1AtnOxlvbUrks12pZts8+8cZPls4FLlEgoNcikrQtVgn7e1sh0IKbYzfA/QNOXdP6Gm1Rd7fzZ68CiU60fLm1ki4txNXf3YA0EvOutjLya9UYSeT8NnClmtpx0d6szo+B79mq5gtpwvIKqtjxYFM3t+WzKuX929VfKg5B1JLePnPMywcHcbNo8I63CrfnfxjDPD3d4zgr+N5LJ5zcb3f8toGJv3fTlQaHX89OK5DX7LzYd7AQI5klnPNUH3ccE5MAHuSixkR4cWepKaloVoLJ1+eiZ1MQmpxDceyylm2vUn8O9rPCS8nS/3hCG8n5sT4o9PR4lJvVIQ7p/KqUWtFdKKIWisiSKScK6xh3fG8FuPB1ujl58KH25KZ1tePydG+DA714MSLM9rUh140OpxTeVUMC3NnWl9/5hrGBDXWzmq0Ov46kYdaq+vy/0lzHv7pKA1akTu/j+P0K7POS/HvoVVHqVJq8HBUcOf4HnzezPgC/M8wQcLDScGuJyajFcUWRWz+PJ7bLuML+lrhlnoZ8iqaQjpzl+3lubl9mNvMGOp0IkmGKTef3DiEsT29WLpRn1xv/EwdSC2hrLbBzJBeNjCIydG+/HE8j+yyOkI8HamsV3PH+AiCPBw4kFpKWkkt+1NKO2SAN50qIKmwmt+P5ljEhC81/jEGeGSEFyMjumdUdGt8uiuFSkMJ17ubz/H5ovZ3y1TWqVkVm8XUPr7trp0dFOLOOpPyK9M41sc7mwzsZQMD2JVUxKz+AQS6OzDtvT1mx0kqrKXfi5tYOCqMF+f3Mz5vJ5Py6U3696BUazmcVkJ+lXmDxqH0Cnxc7KgxZO+fmhnF7JhAfjyUyfUjQmkPOp1IaW0DPxzM5MfDWexLKWHyk/rYaHtifPMGBjKvFUP//rZzfLIzFakAh56dZhEb7kp6+jpzNr+61c7Fgkola47msGBQIMEe5qGLT3amUKXUIAATo3zQ6US8XeworlahkAqsvncMA5pN0nZrQ03Px8XSm31kaiRf7Umlrll1hADtaorKq1SydGMiRdUq4jLKCfJwILGgmjev6M+kKB8yy+rwdbGjQSuy+Uwhq4/mMKW3L29cEcOir2PR6ES8ne3MpqN8uC2Z5fvSGRHhyfJbhjHqje3Uq7XM6ufH+9cOYtPpfK42ETNqD/dP6oW9XNpm3P9S4B9jgC8Vbh0TwTf7MxBFkQendKz765NdKXy5J41NpwvMjOrOpCJOZFdw78SeHcoYmw7hXHUkm5+OZHPgmSn4udjjIJdSr9bSL8CF0/l670StFTmQUtrS4bCXS1n34DiOZpbz9uZE0kqaNFwr6hq4bWw4Gq3IlGg/3B3kDI/w5PovD3H3hAjuntByN9K+5BKeXXuSrLI6HpsWxYhwT+Z28pejcZy6TjQXWu8ONj4yoc1t3tx4lnXH8zieXWGhn1thCANJJQKr43NYvi/9/9s7z4Cmzr6N/zIIey/ZiIIyVFQUN+5Za7V72KUd1ta2tk+rta1dT2uf7r3e1tbVbVtb98Y9qSAqewvI3iEhyfshIRATpgiI5/cJknOSw8j/3Pd/XBer5g1gQn+3drum9LtMvauvmzVPTwni6SlB9F622UBhTgP8dSqTTWfzOZtTxg8PDMdSJuHCZT6DDpZmzBvixauXFUP/81ssh1O0/1e3fHmEJyb1pVgn0rM9Pp//zh1AqJcd6YXVVCsMi7uDfR2xNNOqvi37PY4apXbZvjfhEl/Oj2BGmAe/ncripnCvFm8SKrWGn45nMszfiRdmBrf2V9WlCAG4jXg5WpLyZvsclUf1ceavf3OY3N+wd3bx+tNUK1S42VroXRkulctZ+ssZQj3tWN7EP9Nrc8J4ZO0p/fcaICGvAg97S069NJniKgXejlY8uvYkmUXV3BzhzbhGkoemcLOzYPoAD4b4OzLhnX3UqdW8cdMAgj3sCPOy569/c5j+8QEDmceNp7ObDcBvbT1Ppq69TCzW9ud2NPNH+vPLyWzO55aTUVTdabng1jK+nyuHU4qYYEKDeMWsEPq52zLQx4FXNsWjUmtIulTJra10IDaFnYWUsYEu5JfLWbcwErdGK+JHowL4Yn+qwfFP/hqn//qOr49Qp9Z2HY3t60xWSQ3zhnizZFIgPxxOM7BMGuRtT2GFYefJJ7sbhjQi/exxsTHntggfVvxxlv/8GssTE/uy7mgGvewt+b/7Itj77ARGvLWbf7NKWTDan30JBTwxKZDskmomvLuPOrWGE+klTQq//3PmIi9vOotCqaZSocJMImq363ln0+lqaN2RospavT9aV/DKpniOpxXz2d1D9NoEG09ns/SXM4hFkPTfmbz6dzz7Ewr4Yv4QQnRGhMP+u5OCyyaPLM3ErF0QiYWZhKOpRdwd6ceJ9GKszaXNNv6bIr9cjlqjMSjcrT6UZrQCCve2589mhGs2ns5mw7EMZg/y4p4RfiYHF66Uyto6It7YiVypZuUNIew8n49GA6vvj8BC1vZ1xqHkQl7ZFM+9o/w7NY+YXy7nQFIhswZ4GMgsZpdUM1vXZbDr6SicG6VYjqYWodHQasfiPss3t0klLcDFitTCauaP8GXTmVzKapRYy7Q7rLfmDuD5jXHNni8WQaS/I3EXK3C0kpJV0hCwnazMmBraCzc7c8qqlayYFaIvLJ7NKeMG3c883N+pyRt3xOs7Kaxq+BxIxJDy5qzW/4CdQFNqaNd9AP5sbzLvbE/g0ag+LJvRfdrbqhV1vL8jkWCd7m7Yyu0AjAxw5seHR7Bg9XG9AhVobYga5/acrWUUVSkI8bDlXG4FUrGIw8snGqyE2stvp7LxtLfgxT/jKKpSsGXJOLwcu3bF+erf8aw+lA6Ao6UZJTrrn9sjfHj7lrZLF774ZxzrjmYy1M+R3xeN6shLbTPlciVP//SvXkkt0M2aF2aFUKtU0a+XHRPf24dGo3UteWZqP+wtzdgSl4u1uZSoIFeSL1VwNLWYW4Z6sz0+j+Ub44wGcXydLA3cIFpDW8XyWyJ91Swmv7uX5MJqnp8exKLxgSxad4qtZ/N4dmoQC8YEsDEmm+8OptHbxYav5w9FLBbx9tYLfBWdoldXWzGzPw81syPrCrqFHGV3JFc3SZVXdmWasx2NlUyqF4nPLmnIxU4K1qYQDicbKlVVK9UGNkFFVQqsZRLO6fK/3g6WzfrQFVbW8vOJLGaE9cLf2ZrHfzxNTqmc/7s3wqigdYuuI2PPs63rr20OlVrDnd8cJbu4mp8fGYmPrre2Qq5ka1we4/u7tuqmMb6fqz4AVzbKM/5yMou7In0ZdFkRq549Fy6x8IcT2FuaEfPyVP3jj08IxNpcyuw2VN/bS1J+BXd+cxQ3Wws2PjbKqA7wwc5Edl+4pJeLdLcz58HVJ9AAPz00Ak97S/LK5aw5koGTtYzx/dx4bP1pxCI4vGwSi9adJulSJeuOpnMhz/TwRluDL3Rs8AVDDeS3tyVyKqOEL+6OYMK7+/hwVxLxueVsjdNOcqYVVvHsrzGUVmutpWQSreymtUzCg2Pa597cFVz3AfjFWSFM7O9GqIedkWZsd8Hb0Qp3W3PyK2q5pEs5yBstYJytZZRUK4w82jztLdAAYwJdWTk7pNkJr/d3JrLhWCaHkgv54u6hbD2bh0aj3QZOuCxnXVatbLEK31pqlCpiMktQqjSkFlbpA/Db2y6w7mgmE/u7tehyCzCmryteDhbklsl59cZQcsvkfH84nVqlGvMmJtZe/ussa3TjwiXVSqpqlbz81zmyS6r5/O4hLJ/ROYWct7acp7BSQWGlgi1xuUbuGEP9HPnlRBZ25lJyK2o5mKzt7+7tYk1QL1sOPDeBD3cl8k9cLlFBrng7WuLnbIWthRQHKzNGBDiTWVzNhbxKfRC3t5SiqNPoi17dkV3nC3jxz7PI61TUqTV6uyA/J0syimvYGKN1UtnTaCc4e5Bnt/wMN8V1H4AtzCRYmkkZ87+9DPJx4LdHu3a7aYrF60+Tr2sBmznAg+raOoPVR1GVsQCJTAJJOuPLTY+PaXG8NirIld3n85kU7I69lRmf3DmY3FK53vq8nq/2p/DW1gssGt+H5ztgItHGXMqaByPJK68xeK8BXvZIxKJWidKDtoNg+9NRVMrriE4s4Kv9qSwaH8DdkX5NVs8bq/C9MSeUOrV2dBUgJrPUpJfZ1eD+0f5EJxViYy41qX4WFeTKiABnIzH3vY20LZZO7cfSqf303+9vNP33+k1hPBIVwGt/n2N8PzeCPWz59VQ2F0tr9FOUw/0dOZ6u7apxtJKiVHXN6P/X84fycKPC8v7ES9wU7sXcIV6Eetrz3PT+DH19h8lzn5kSyBOTgkw+11257gMwaAsfSpWGk+kl3PDxAf56fEy3uoumF2m3jSK0fcHD39zZ5LFRQS4smxHMA6uPk6fr5/079iK3D2u+X3daaC+mNbJob6rxPbtEu1XNKja2GW8PdSo1x9OK8b4sh3z7MF9ui/Bpk3iKjbkUG3MpMVklKFRq4i+WN9u6tHbBcB5ac4q5Qzy5RycK9f5tg8guqWF8P9PdImq1hi+jU3CwlOk7Vq6UcUFuJDfTWXPfd8c5nVlq8JijZfMf3Qnv7KWoSkH0f6JwsLbA29GKr++N4HiaVoe4sUnnf+eGMXewF3d/c4yYrFLKaurwc7IyGYBlkgYXjI4mduVUZFIx80f4YWshoaymjvXHMvn2YBqe9uYEudkS9c5e5E2Myt8xvHsPXZhCCMCAp0PDh/TsxXKKqmo7pFjVUfzx6Gj+szGWGwd68sXeZIqa0Vzdn1jIo1EKKnRFKEszMTe2YVKtJVbMCmZsoEurK+4tsS+hgA92aV0ZJoe4GzgUt1e5atn0YMK87JnSgkuuv4sNO5dGGTx2+fb/co6nF+uNO8f3c21Tu9vm2FwOpxTy7NR+Rh5wTaFSa4i/WA7ALUO8ePe2cGrrVM1KdUYnXiKtSHuDvPOb42x9ahyXyqsZ/fY+LKRiffAd29cFV1tzbhjoiZVMyvqHInl76wXWHMnQn3857Q2+UpG20JdaZDrXLAJsLaSIRCL9sFFqQSXrj2WiAV7bfIHXNl9o9j1sLaT6ibprBSEAA7vP5eu/nhnWq1sFXwCZTEJsVhl//Xux2eNEIq0B7sI1J6mpU2MmEbHn2fFYtqMNqykszCRMbbRSvlIG+zoQ2dsJHycr7Cw65jrtrcy4O/LqrIZCPO0YG+iCvaUZbm2ctnvxzzhKqpX4O1ubNPo0hUQsYu2CSFILKvWCNS3pJA/3ddJ3KNT3645etRelGpSqhpzvtDB37mkkdmMlk/LqnDCcbcz5aFdihxp6ioBlM0MM0guN0QCf7kkySCH0vkyEvTnSV80i4o2dFFYqmDfYi/e7ke1Qc1xTcpT1yJUqHvz+BA9+fwJ5BxQRDqU0TIc1JTHY1aQVVTX5nFgEy6cH6fUUqmtV9Hax5pt7I3jyp395d3vTK4fVh9J4YPVxg06LVzbFM+ezQx2WZmgOZxtzfn5kJO/eOqjbabWaws7CjLULIvn0riFtdvZ+NKoPY/q6GKR6WsPw3k7cMdy31eaTFhZS5oR7IhahdwJpXKAdG+jC0imBTd6klkwK1EbMDkSpgamhvYj+zwRuaaTF3PhtrC5bKIhEImTSli8k9pWpFFTU6g0G/vw3p0OuuTPontGmBdIKq9hz4RJ7LlwivZnA1FoWRTWsRuYNaf/0UWu5WFrD2iPplNUoWz5Yx8rLlPxH9Hak3izBzdYcD0dr/WpHA6QUVLH2aAbH04r5dG9KkwWVj3cnsTehgOUb4/R+WRuOZ3Imq5TDKYVt/tk6g4U/nGTAyu3EZpcaPbdqy3mGvLaDrWdzO//CWuCRqD6sWxiJr3Pbt8if7U0iYPlmZnwU3fLBwD+xuag1sPN8vtH474GkQlILqlhzJINNZ0zvqrY8OZbGa5HFE/ogFUNvZyveu3Ugy2b0Z0qIG242rUuleOnSfL7OVrx7WziHlk3kyPMTWfvgMDzszblhQC8WjDXcFfx2KhtFKxy7B76yg78b7Q4jfB15b0dCp4+kt4drMgUR7GGnD0j1Nt5XwqxBXlyq1OqnjglsmwdXWyirVnImu5RvDqRyIKmQhPwK3ripdSacD4zuTeqlCtYe04pXT+zvxtE0bdU6r7yWD3dewNfJkknBbgz2dUIqFmFrIeVwchH+LlZYN5qqOp5WjKeDtjCzcnYoX0encCCpkIulNex+Zjyf3DmY+Jwy5oR7mbyWruZ0ZgkVtXUk5FUYmTB+Ga0dsV32eywzrkCLt7ux9kgGag2cz61o+WBgxoBebInL4/YIb6xkUib3d2HXhUL8nS3xdLAi3MeBlZviARjR28moWNnP3Y5TL03lmV/P4OtkxX+m9UcqFvHR7mQ+3J3EF3cPRSyCBaMDuPe74yh0wc7OQkK5rkfS2VrGncN9WX8sg//ONfw/99Llzm/56gi5ZbUMMyG0VR9Ah/k78uujo5jw7l7SCk3vyl7brJ3ODPe253hGCcczSvhsbzK/PjoKb0dLVm29QFSQKzcN7l7/09dkAAb0zg4dQWZRNe9uT8DWwozbh/lgbS5FrdZQrVTpDSEbczSliNWH0wjztGfB2N5GW6emeGzDKQ4lFzG6jzMWZuI2jwbXF2MADiQW6A0LAVILtcUNc6nUoOh2/vXpBq+x61w+C9ecxMlaxskVk7hpsBc+TpY888sZvazk5R0R3Y11CyK5kFfe7A1C3f0XP23i3dsG8fiGGEqrlfgv28z3D0Qwvhk96U/uHMIndzZ8P75/L05mlvHo+L7cMcyXkioFf/x7ERdrWZOSlnaWZgb6C7MGenIkpZgZA3qx5McYUgureGZKEKdfnszne1Pwd7bm5qHeTHl/PyVVtfz1+Gi8Ha14dlo/k68P2vTKrvP5Bqap9dw53JcBXvb463LBO56OYvnvsfx2Wpti8LaXkVehxMHKTJ9+aKwXrtZoWw0tZRL+iMnhQFKhEIC7IxW1SqqV2mbv1/6O5405oSzaEMPehAK+uXeogetvVnEVd3yjteTZHp9PYWUtr85pndVJfXHvxnBP1l/m3NoSmUXV+DpbczpL+w92IMW03YxaoyJs5TYqa1VMC3Hnq8sETBytzZCIRRRXKXj+9zjeuXUQQ/2c9K4R1Yo65n1+mGqFSmeG2HUaGU0R4mlHiIkPbOOURIinsUvEtcyYvq68NTeMRetjAHhg9Un+WDya8CYm/C5nX0IBpdVKjqUWc8cwXxytZQaKfK0hyN1Wr8eQVybnr38vEhngjI25Gc/pesJzy2pILdSmBTOLq42kNy/ngxaKZWFeDX3gZhIxL98YikwqITanlLM55fpBnfO5Zdz25RF8na2ZFurO2iMZDPN3ZMGY3pzOLMFKJqF/r87ViW4NQgAGQj3t2bR4DLM/PcjPJ7P5+WQ21jIJKrWGtMKGHHPgC1uMXGAbu9YWVtaiVKn14jVf7Evk7W0Nmr0PjenNyRcnt1li8N5vjxKdVIRUBIO87DiTU97ksTnFtVTWareAB03kcIf6OfHw2AC+2J+iN8BsTEm1koT8CjQayC2Td8sA3BSWZhLEgBqoU3f/gl5bmRbqAWgDsAZtAfWjOwa36tyVs0MY4ufAza0wBG1MQUUt3xxIZVqoO0P9Gpwlls8MNqnS52FvyX/nhlFarWTEVdDvtrMw4815AziUXMhHu5P0QknBHvacWTlNX6hcMimIGp3eRUpBJdUKVbMeel2FEIB1DLhs4qpKocLOUspHu5KYNcATZZ3aKPgCxGeXsf5YBnmlcr6MTkGj0U6ehXja8cPhTINj9yYWsOKGtttiRydpuzTqNPDDgkgKKmqZ9clBFHVqfnxoBAGuVjy4+oS2fejOcIr+r5ZzueV8ZcKxF2Dp1CDCfR1MpkC8HCz54YHh1ChVBquP7khxlYJ/Yi8yLbQX7nYWBLrbEu7rwOnMUoNBg2sdhbKOAa/uwFZm2H52KKn1RVIfJyseG982/WqAbw6k8nV0KtGJBWx7qmXdY+CqtQA2ZnRfFyMPv8ZdIoWVtUz9IBoR8MfiUdQq1YzQ9a6vPZrBoaRCXrkx1KQNU2ciBOBGjOrjxGHd1r6vqw0ZxVWo1Boqa5VsO5tn8pyj6SUcTS8xeOzzfcl8etcQ1i2IZOqH0UjF0MfVlu8fGN6u6+rrakNyQSU3DPTAwUqGg5WMvc+Op6iyVl+E2txIFPynRwxl+7KKqzGXivWFFjOJuNkc77ig5jWDrwYLfzjBv1llbHgostVuIW9tOc+vp7KJTizgzbkDcLOzoFTXWZJaUMnm2IsG9jlypXYV1F1vLBuOZVJQUcvjE/vqJzG/2HOOt3ekAVBbZ9jNUC6/+qPC00LdiU4suCJt4q6gRqFq6DLSiHhiUqD+uXe3J1BWo2Son2Or+7GvFtd9AN5zIZ+nfz5D/162fHKndjtXH6jO55ajVKmRisWsP5re6tccpAuKfd1tSX3rynVJdz0TZfSYl4OlvpLcHEn5FbqbgIgTKybjYNW6tqHORKPRcDiliGqFivO55a0KwPsTLnEoWbsCPJ1RyvA3d/PirGDEus7Scnkdz/0Wqw/AFXIl4/63l5JqJctm9OdEWjGHUwoIdLfjnkg/bhvWtQHmUrmcF/7Q6uoO8XNgrE44vz74Xo6dhZQvmzG87CiG+jm1euXbnfBxstLnuC9v+3thZn8OJRcxZ/DVV7pries+AB9KLqKsRsmxtGJWH043EJgJ9tAWekJf3kZVI/3U/r1s+Pa+YbjZWWAmEXMwqQA3G3OszKU4WMtMdk50Ff/E5qLRaO2IzmaXMaYLVrctIRKJ+HZ+ONHJxa02X1ykcxEBKNFZ+sRmFZNcUIlEpN2O9nG15n/bLvD0lCCW/BhDSbV2RbRqa8NgSmx2GZ9WJxsE4MqaWoa/uQd/Fyu2PGl887sauNiYM2+wFwWNdjVNYSYW8X/3RzDcv+s9ErszTe10bh/m26I2SmfRfSJFF7FkYiCpBZWkFlYxOdjYLgYwGNcJ97Hnz8WG7g9jWrD56Si2xuXy7o4ElkwKbHWP7k2DPflyfwoyqZhBvg5X9wLbwenMEt7fkcBBnVfdV/vTSF3V8q4hzNNOr95Vn+3dFKsdKVdpQKXSEJtTTmxOOV4OlgS4WLE3wfh13GzNWK4T4i+qrGXvhUu8/NdZqpVqzuVWEpdVwgCftrULtgexWGRyfPbL+UNYsfEsA73s8Hay5sUbglscRe6uzPr4AOculvNCNxRM7yqu+wBsb2XG6hZys38/PoZDyYXcGuHTJtPMjuaf2FxSCqr4+0yuQQA+n1tGXzdbzEyMxvZ2sSHhjRmdeZltYuVf8cQ17t1s5Xm/PDqKqHf2ktGEaEw9/XrZsOLPs/TrZcv/3RfBwh8aHF3cbGTYWZgR5mnXpE2Ph0PXFmmmh3owPbT9AyX55XLWHsnghkEe+qGlC7nlrD6UxpJJgXi10CbWkSTlV6IB9iYUCAFYxzU5itzZBLjaMH+kv1HwLa5ScMsXh4hJa9ppuCN5bno/HhkXwPKZDWmS8f/bw4yPDhLRhEZqdyfQ1Vhw5XBSAZtjL/JnTPMz/YsnNP8hXjkrmKVTtEMAGo0G50Zjsz6OFlyqVJBcUMXYd/YZBd+nJgVy4PkJuNi2Tu3scEohi9ad4myjm0l34KPdSXy6N5mX/4rXP3bXN0f5+WQ2968+AUBGURXTP4xm2e+xV/VaPrlzMNND3fn4zvCr+j7XEkIAbgPFVQryyhoMBYe8vpOTGaXM/epop7y/n7M1y2cG08e1YcggXWclUybvvs4GTTHh3b1sNKHwJpWKWbwhhqd+/ped50x3nwDcFuHL6mbcMl7dfB6lSs3Op8fxyyMj+WR3Q0+2vYWho4ezteH3GjT4tGF1+OmeZLaezeP/DqS2fHAnMiXEHX9nK2YP8mTdkXTCX92BTCfyUC92/+rf8VzIq2BjTA5t8Yi8nBUbY+n7whaW/WYYyLfE5jLn04O42Jrz5fwIXGwMdxWx2aXklLbdEqmjRbm6AiEA6yipquVsTqnR43UqNQ+sPs64t/cw5u09jHtnLykFlRRV1hoct/D74510pYZM6KfthbSWXVt5wYVrTujn+qWN/gs/vWswTtYyfdpd1IIs1+V2SZez4o84Cipq2XA0gz0XGqxrzuo0FUI8bPhq/lCOvTDZ4LyNp9umqPVIVB8m9nfjvlH+bTrvajOhnxv7/jOB+SP8+DI6ldIaJXKFmnOvTeO928I5mFyg/73MDOulF3RqD7+dzqFOrWFjTLbB48/9doYz2WU8++u/RuccTinkxk8PMevjAyja+N6NRbkaD0xdS1z3OWDQbk9HvLWH2jo1y2f055Gohq1tWY2SfQkF+kKPRCxCrdYYzc8r6rrmDhx/URtIrlTJsaiylvwyOSFXuUe2Qq5EKhajaKSPWKducNj9/lA6JzMa+qoXrjmJu60Zx1ZMNX4x4A+dhZBMAgoTf4Kymjp+O5VNTFaJ8ZPAudxKzl8sx83W8O+5/ckxJo9viqggVyP7ptayOTaX87nlPD6x71WtMbwwI5g3t57n4XEBev0Sv0bi5RZSCf1f2saTkwJ5ekrbrX0WRQXwVXQaC8b2plyupKCilk3/XqRSoUIswqQOg425FJlEjL2lWZtdaBqLctV3LF1r9NgArFZrkNepDIRySqsVvLM9gcgAZwPBGrUG6nRJwPqWpnqcbcz5+M5w3tpygTqVms/vGUqgrk/VzdacSxW1OFiasWah4fBDZ1F/vZfbjLcFhULF0Dd2AbB0chBLJge2cEbbKZcreX97Aj+fzMbe0oydS8fx6NqTHNINvphLQV6HQfCtJ79Cyfi3trBxyWSjG9/LOkWv5n58ZxuZ3krJ3kLKGzeFsfSXM/rJxg93J/Fho/TEoqgArCw6p19ao9Hw5E8x1Kk1+LtY6x2nm2P1oTRe//scw3o78vMjrfcwnDnQg5kDDQt6Pk7WJL42HYVKxX917XntHdl9ako/ntLl3Ce9t4+UgobOormDvXnShF/bQG8HDi+fiLVM2i4bsI4U5eoKemwAvvObo5zOLGHdgkgiA7T9kr+fzmH9sUw2nbloEIAlYhGbl4whLqfM5MTP7EFezB5kfPf+87HR/HQyk4fHdl1F96/Fo1n6yxlevqH9Dr41jVwSMks6VoQ9aMUWFCoNUrF2pQug1mhQ1KlZ/9BI4nPKKJMreXXTORLym5ZaTC/TMPezg1wsk+NuZ8Ers0MpKK+kQjcNZmUmolppOn/Zr5cdYwNdOZhUwJfzh2JrYYZUIkKpc8FuPLZsaSbm+U5yQwZtD/Q9I/yIyylrtc3Ta3+fQwMcSzO9qm8rMpkEGRJenBXCyD4uRHVgW+Vtw7xZODagWdGgtmqj9CR6bADOKKpGqdJwsawhuT81xJ19CZdMOs/297Cjfxu3MZ6Olvoqe1cR4mnP6geGcTytuEWvsKawt5TxyR3hxGaXtVmr4ufjmTy/MQ6ZVEyirt1t69lc1hzOYHpYL72HWJ1au90dGeCEVCph+cY4Vt08kFAve3LLakgpaFnnNkNXcMwuqWHhmpMGz5kKvv+7ZSAjA5zxcbLilqHeqNUaxGIRA1Zup0aXAlGpNdjIJFTqltD14i6dySs3hrbp+CB3GxLyKzGTtH7FKFequOubo1TVqvjp4REmPemszaUd5h+48bHRFFTU0tetZ6nSdTQ9NgD/+uhIki9VGhRpfJysWLsgsguvqmmqautIyK9gsI9Dm615Fq8/zenMUp6aHMhTk9tnyz073IvZ7RBg36xznlDUqalRqBjy+g59cDuSWoSDpZTSGu0qdc2C4Xg6WBK4YisAMwZcYu5gb6zMpDhZm6NQqSmtbt4lRCyCkQFO+tRFc1zIrdD7qEGDWEudbsXbx9WKlIJqffAFeDiqa7UBWsP2p9s+nVdUpSAmqxSNBjKKq1s0BT2SUsRbW89z/yj/Fo1KTWFvaWZgsCpgmh7bBeHjZNVihbw7sXjDaeZ9fpivotvextTfww6JWESgW+frnX51TwTBvWyZGuJOTkm1PvgCPDDan8/v1uoVSMUiopMKWbD6KFLATCLik91JzPzoAADRz03g8LKJfHz7oGbf79v7hvHZ3UPxd7LCy8GSvx9vWtO2qcKbi67gdqnCsJNFDEYtUj0FLwdLvp4fwYe3hzebDhj51m4CV2zhq+gUYrPL+PF4Jh/tSuLe744btGAKdAw9NgBfa1jp2sja0072xpxQHhrTGxvzzm9FU9SpKK1RsuNcPmcvlulNTecN8WDl7FBG9XXhy3uG4O9izUt/niU6uYQ6tNoUqYXVnMst51BKIRZmEqxkUm4c7I2LVdMbs5c3neVkejHpxdVcLKvB3lLGLUO9cbDSrrZCPe3wd7Zi8YS+fNyEVu6mxWN4NCqAist6px2te/aKbUqIe7OOEGWVCnLL5ChVGnJLa5g/wo8XZgbz2d5kohML2HPhUidebds4lFzIs7+eIaMDPCI7kx6bgrjW+OiOwTw3rUZvv9IWXvornvXHMvn6QGqHqK+1lsTcMqZ+dBDQFjLd7Cy48Lrx2LO5VNJsZf1MVgkzBzRU5z+7J4LbvzY93JJVXIO8Ts0Xdw9BJhVrTR5vHYSiTk1MZgmDfR2RSZtfV2iAL/cb7zRemd12reaeRIm8If0T6mnH6zdpnV5W3TyAf7NKuWFQ9/XYe2vrec7mlGMtk7TaoaY7IATgboKZRNyu4Avo7Xk6U4Wtrk6tD74AX98z1GRxU65U8dPxTKPHG3MouYjvDmqNSof1duKx8X0ZG+jCgcsEx1+c2Q9zMykzwjyMWpZkUrG+26UlJCLjgp2duZjZ4W3PdfYk/F2smTfEi/icMp6a3FBcnjfEm3lDvI2Gj7oTC8cE8POJLG4Zem3pFnd6AN6bcAk/JysCXIXq6JVSVqNk/rfHsDST4GqtLXalF1bi73L1f7dfRafovxYBMjMx9357jBdnBRPUyKl63dEMtp/Lb/a1zl4sp6CilvyKWo6mFvPY+L58PT+Cu745SkxWKX5OFrx/+yCG+nWMY/UD3580ekyl6XkWRu3h/dvCTT7++j/n+PZgGksm9e2wzp9yuRK5QmXkyNwebhrs1e0MN1tDp+aAt8Tl8sDqE9z8xeErmjlvLxVyJRXy5qvs1xLphVXEZpdxLK2Ygqo6lGq4vZN0KRq3a80Mc2fx+tNEJxXy2IbTBsdF+DvRy86CIb4OzAn3JNzHeNLOWibh9mG+3Dnclzd0215LmYQ/Fo8mfdUs9j83qcOC758xOZzOLDV6vKtcoDeezubJH2NQqa6OjfPJ9GJu/fIwf58x1txoC9t03S4f705m/rfHWnXOO9svMPn9/SYFiuRKFVPfj2bM23s5d7Fpj8OeTqeugD0dLDGXivFztm5zq9WVUlRZy+T396PWwM6nx3XIXberGeTjwP9uGUidSs0Lf5wFwMaicwpxdlYy0hvp9j64+jh7EwuYHGxolR7u48DRFyYRnVjAwh9OMj2sFwO87Fl7tCEtUaVQUVlbx1vzBlz16x7gbY9ULEKMhpdmhzGstyP+TlZYyLomG7f0lzOA1sOsrU7ZreG3U9mcSC9Bo4HZ7ezxVarU5JQ2dEAcSCrkfG4ZwR7Nj63/GXORnNIaopMKjMTRNRqoUapQqtXUdtEYf3egU//rwn0cOLNyKjITurVXG3mdmsraOjQakCuvzmrjSsktq8HZ2rzFIlJj6vtcvzmQSlphNelFbVeVaitFlbVcqqg1mL//rpGmslKlRiwSGeRpky5VolCpib9Yxu5nxnPDIE/9at3D3pzHGklLqtUa6tQackpr+DMmh9uH+eDZCvul1tDH1YbkN2d2yGt1BBKRVkA+sJU+eG3lkag+aDRwS0T789tLfowxeuzTPSl8dveQZs/74PZwDiUXmhxusZRJ2PbUWMpr6ujXDe3iO4tOv+13laC5l4Mlm5eMRa3RGHlEdQe+3p/Cm1sv4Gwt49RLU9p8/pKJgfznt1h62VtQXKUw0kzoSGZ/oh0J/v6BYYzvZ9hrnVtWw6yPD2JrIWXrk2P1Whz3jfTDxUam70GNbGRZnltWy7HUIhQqNTeFe3HjZwdJK6gixNOOE+klZBZX84EJt4h6NBoNRVUK/UjrmFW7KatWsuHhkUZu192N+Nemk18ux8+5fQXYlujtYs3btwy8oteIvyxF4Kdr82uJ4b2dGN7bqcnnPewtaWER3eO5rvqAg9xt9a4A3Y1EXZvW5WJAzfFvVimjV+3hlU3xzB3izTB/J7JLanh/pwnvnQ7E3EyCWITJlXpRpYLiKgUXS2uoqm3YWkolYuaEe+HnbE2NQsWaI+kNrycRsXhDDE//fIbJ7+0jPqecKoWKUE87vBwsmRLibvQ+jVm5KZ6IN3bx7cE0nvv1DNmlcioUKmZ/epBL5Vd/R3Al/BGTzXs7Ov7vJVeq2izv2BSr5g0g0L2hsJtVXM3Mjw8Q8foOnvgxhvJm6iqZRdU899sZDicXGj1XLlfyxI8xvL8zsUOu81pEaEPrJqyaNwBnaxmjWinIAnA8rYic0hp2nsvnlRtDGeRtR2x2qcl2sI7k7yfGUFqtwNuEYHmYlz0bHorEWibF1dZYZMV/2Wb91zKpCEWdRq8XAZBS2CAGtGJWCK/c2NDTWVRZi7W51GgXVT+hdalczvAAJ3451aBHezy9mBsGdt/q+PKN2tx9Ze0JvmtGXL4t5JbVMOOjA1jLpGx/etwVtyeO6uvCzqej6PfiVmrr1NRrFxVWKfn7zEWmh/Zi1kDTPcLfHkzll5PZnMst558nxho8dzCpUF8cfGRcANbdyMy2s7j+fuJuilQiZvnMtqlwzR/hD8Awf+027/sjGciVanaeyzMYbOhobMylzX6oW3sDuC3Chwg/R576+YzB42IRjAhwNvC4i80u5ZYvjuDlaMnupVF6XQeA924bxMmMEkb3cUEmFTMzuBf3fHcUBxuLbh18QfuzqjUNvdwdQUmVkrIaJdW1KqoVdR3WH/5oVACf7UmmrlEDU3AvW8b3a1o9bd4Qby7kVXD7MOP+3KggV+4c7oO/s/V1GXwBRG1pB4uIiNCcPGncQynQ9WQUVhH17j4AAt2s2bl0fJdeT1PUy1P6OFiQVWqsLWAtk/DH4lEEuRsGpEPJhdz9f8dwtDLj+IrJJg1I28LKTXGM6+vKpJCuaT+rR6FQUSpX4GbXMUXGek6kF2NpJmnSmv1KuX/1cfYlFOBqa465RMyGh0fg69T9aivdBZFIdEqj0UQYPS4E4GufxetPsftcHo2lDXY/E2XgHXc1ePKnGP45k8tjE/rwzNR+yJUq/onNZVQfZ2wspJzOKGF0XxeTwXLCu/tM2shsXTKGYE/TQePfrFJcbc3xusKOiJu/OMwpnfB741a67s7m2Fx2nMvjuen92/w7uO2rI1TUKPn9sVEGJgVt5duDaajUamYP8uR/Wy/wh87Tz8vBgoPPT+z09tJrhaYC8HVVhOuJaDQaNscZBl9na5nJ/GtHs+tcPiqNhq1x9U36STz76xme/CmGpT+f4f7VJ5ossHx//zDsLQwDQXAvW1Zta7ogFe7jcMXBF649/7x6Vm07z1//XuTnZka7K+V1lF5WyD2WWsTxtGLO51Xw47Hmx8KbIzG/gtf/OcebWy5QVKlgdyNxnpxSOZW1de1+7euV6zPx0oOoqlUxPdSdbfEN4757nhmPncXVV/Z6/aYw1h7J4LWbtILiIZ52SMUiBng5UK3QfhjddTeCoa/vpLhKwSd3DuaGQZ74uVhz5pVpACTklrMtPpcPdiVzPq+Ck+nFRPgbti8VVtay4Vgm08N6EXSFPbNrFkSy7kh6sy1S3ZEnJgay/Wwec5vQ580trWHM23tQacDH0YI1C0bQ28WaYf6OhHrYUVlb1yrLo6bwc7ZiWqg7KrWGvm42jO/nxiZdEW2IrwO2nfA/19MQAvA1zvD/7qK6kSW3BLC36pwPQr1ISz355bVYySRUK+r46UQWLjYy7td5dhVVaVdlR1KLuOGyiax+Hna42lrw04lsbMylhHs7GL3XJ7uT+OFIBtGJBfy2qPU+aE1xz0j/K36Nzua2CB8DgfnLKalWUN9QklUi52R6Mb1drBGLxWx+cmyT57UWc6mEr+Y37KIfjeqjD8Af32la+lOgeYQUxDWO/LIxTjVafYGu4GBSAeXyOo6kFgENQbdxrtfJSsrCH06SUmAoT+lkI+PI8knsXBqF1ER/8fh+bng5WDI9rGuLZt2ZEE97ProjnPtG+rFsRn/mtMPhpCXkShUn04tRqzW425njbC1DBDz/e2yHv9f1gBCAr3G+uGwcVAPEZhuLn5ji91PZPPlTDPnlLTsdJOVX8NneZEqqmh4Uef2mMFbMDOanhyK5Y5gPn+pWRb5OVtwzwpd5Q7zYHJfPrvP5/HIiq1XXWM+E/m4cWjaRhWO7v2VQZ7HhWCbj39nL7vMN6ac54V68OieMR6P6tGmkvTWo1BoWbzjNLV8e4YNdiTjbmBPiaYcGrYWRQNsRUhDXONPCDPt9Z4b14tlprZMLfGvreQorFfTrZctj45sfLV22MY5TGSUUVtaycrZpE0lvRyseGqcNkKtubhh/lYhFvHGTVmhna1wu/8Tlclekb6uuUaBpNsddJL2omu3xeUwKbn5asCNY8lMMu89rC2/1nRSTgt04mlLE6L6tHyASaEAIwN2cw8mFPL8xljuG+Zqcv9+XYGgT8+a8Acz4KBq1GrY/NQ4bi6b/xE9PCWJ/QkGrtqqTg93JKalh3BVals8Y4MGMqzgkcj2QW1bDT8ezWDCmN+E+DtzTSU7OBToPvUfGBbBofB8UdWre2nIBpVrD/iTjUWOBlhECcBdSWFmrzaE10zu5P7GArOIatp7NNRmACyoM0wfv7UggS2ffvi0+r9mq992Rftwd2boP76LxfVg0vk/LBwpcdd7ZnsDG0zlEBbnyw4PDWz6hg/h6/lDO5pQzqo8zu87l8fDaU3plw4B2urlc7wgBuIv44XA6KzfFc1ekL2/ObVoH99GoPljJpEwOMe3w3NfVcPpo7dFMgtxtkIrFzOjgglVemZw5nx7E3sqMbU+OMxgHFug8TqQVA9qbc8DyzZ3iAyhXqlh7JIOBPg6IxSKWb4xDrdHKvCa+Nh3ZNdpb3dUIRbguIk9X+Mpvwerb0VrGk5MDCW1iOuyFP+KMHnv1xjC2PDm2w+fr9ydeIr+ilsT8So6kGm855QqhEb8zuNjof0bd+kHWK+KPmBze25nIonWnAJioyzmbSURC8L0ChBVwF7F0ShDDezsxxNfxil7nycn9eHRdg2D2qnkDGObvyOHkQsJ9HVo9dqpUqamtUzcr3DIn3JNvD6YBMPCyXt0ZH0ZzPq+Ce0f68do15Ep7LfLWvAFsirlIYn45kX06Z5gksrcT/XvZ6odX3r55IK/PCUMmFfPin3FsP5vHsN5OjAty5Y5hQoG1tQhaENc4N312iH+zSvXf+zhaMnOAB19FpzJroAef3WXYprbzXD6LN5zmrmG+vDJH282gVmuY/lE0GUXV/L5oVLsEXAa8sp0KeR1D/RxYv3AEJdUKPOw7VmBGoPPZdS4fiUTEhH6mU2BgKDEKcPLFyXpxfAEtghZED+Vyk9HiKgUOVlo3DEcTE3HfHUpDUadmU2yDSaNKoyG3TE5tnVo/PNFW1i+M5J5IX767bxh3/98xRq3aw84W3JAF2o9cqeK3U9nklnW84HyFXMn+xALO5pSxcM1JHvz+BBlFxsJJppCKRVh3kb/etYjwm+pGyJUqxCJRmxro/3xsNJ/vS+aL/akAPKxrEZoT7kkvE8ajIR62HEkpYlAjqx4ziZhNj48hr0zOSJ0g/IXcUv63NYHj6SXMHerN643SCikFlRxILODWCB99nnmgt4M+LVEhV6LRQGVtz3Gg7g7U1qnYEpvL9DAPPt6TxBf7Uhje24lfHhnZoe/zzC9n2HEun/tG+dHbxRoziQjnZla0T0zow6d7U+hlZ8HOpeOwFHLCrUZIQQA/ncgkIbeCl2eHdJmcXvzFMmZ/chCJSMTh5ZParGaWW1JDhUJppKN7OXUqNSczShjk7dDkB+W3U1k8+6vxaOkfj43Ezc6SmR8doKxGyeMT+poc+iiuUpBeVHXF+W0BQ/q+sJk6NUhFcOswH34+kcX9o3rz8uyQDn2fF/+MY93RTF66IYQFY3q36zV+Pp7JnIGeWDTTh3490VQK4rr/7ZRUKVj2u7aTwNZCytKprZsi62iSL1Wi1oBaoyGvrKbNAdjGUopzK86RSsSMCGh+aikpr9Lk47d+cYTHJvSlrEaJCBjqbzrAOlnLrqop6PVKvcVbnQZ+PJ7FTeGeHR58AV6fE8aSSYG42RrvoFrDoFe3U1ZTx7KNcaRdQ3rLXcF1H4BtLKTYmEuoqlU1GVA6gznhXuSUVGNrYcYAE2pgzXHuYjlzPz+Et6MlO56OMrCDr+dShZyiSoWBlXxTLJ8VTHpRFbE5JeSWNeSE6zQQGeDE9PxeTAp2a7YwI3D1uVqbNZFI1O7gC1Cj0ApEaYDoxALGBV3Z9GRPRkhB9ADq7XpszaWceHGykWmlSq1hxFu7KaioZf3CSEb3bbtp5+pDaUjFIuZfgzKOXY1arWH9sQy8nazaddMqqVSwaP0pbh/mwyt/x1NWU0dfV2v+WTLW6G/dHSiqqCHizT1oNOBiI+PHh0YQeIUaztc6QhdED2Z0XxfenBuGq605G0w4HogASzMJErEIC7P2/ckfGN1bCL7tZPeFS7z0VzwLfzipF6pvC3O/PMTRtGKW/nKGshrt+VklNd0y+AI421pipbu2wkoF8z4/xIe7EmnLYu96QQjA3Zy8Mjkf7koktcB0XraeH49nkVpYxaptF4yeE4tFbHlyLAefn8BQv2vLBaInEOJpR183Gyb0c8OyjUGzqLIWmVj7MbUwk+Csy60vGO3f0ZfZJDvj8+jzwhZGvLmr1eesXxiJp4M2jVFRq+LDXUk8vuH01brEa5brPgfc2ajVGn48kYmvkxVjW6Es9u6OBH47lc3pzFLWNCO8ct9IP17+K54J/U2/ZktW8gJXDy8HS3YtjWrXuZ/sSSbxkvbma24m5tRLUzry0lrFZ/tSUKk15JXXtvqccF9H9jwzngdWH+dIqla7YnNcHgHbE3imlXKp1wM9YgWcU1rD9vg81J01GH8F7Dqfz4o/zvLA6hP6YkVzTA7WOkFMDWle7/WWCB/OvT6dz+4e2lGXKnCVWHMkned/j0WtVrd47IT+brjbmeNkLWP2QM8Wj78avH/bIJytZYwNbFvtwMJMgvSyQuEne5Nb3M1dT/SIJdF93x0n+VIlr94Yyn2j/Lv6cpol2MMOf2crAlxtWpWPnR7mwfSwa1s/d8H3J9ifWMDz0/vx0LjrW9KyQq7k5b/iAXCykvH8jP7NHh8V5MqxFyZ3xqU1SYCrTbtX3kP8HDmQUmzwmGDe2UCPWAH3drFGKhbh62zV8sFdjI+TFfv+M4Hv7h/WZUMfHUVJlYK3t13gRHpxs8cdTimiTq3hz5iLRs9NfX8//VZsISG3dTZK1zrWMgkOlmaIRTCqh7tIJOZX8PGeFKPH29rj3pPpEW1oGo0GuVItjEB2Mu/tSOCTPckEuFqz55nxTR731785rDuawX/nDjCwlM8vlxP55m4A7C2lnFk57WpfssBV5lK5nG8PphGfW8ap9BJqlMZplkfG9mb5rI4fIOnO9OhJOJFIJATfLmBSsDs7z+VzY3jzuck54V4mbY/c7SwQi0GthmmhPdvt+GxOGRZmYvq69ax+2OySam7/6ih+zlasWxDJyFV7ULVQi/nqQBoTg92JbGEi83qgRwRggY7nYFIBCpWGif2bHhwI93Fg21Pjruh9Ut/s+aOqCXkV3PjpQcwkYg4tm9ijpBqTLlWSU1rDpQo5ZXJli8G3ng92JvDTI6Ou8tV1f3pEDligYzl3sYx7vj3Og9+f4EBSQVdfzjWPtbkEa5kUe0szzDvYKr6rGR/kyru3DuKHB4bjaCXjvVsHtSqoHE0rEfqCEVbAAiYwN5MgQjvL31pHDYGm8Xa04tDyiUjFoh73+xSJRAbGrzcP9Saqnwvrj2Xywc4kJCJYODYAO0sp53MrWDi2N3M/O4wG2H1e0IvuEUU4gY4nq6gahVpNH1ebrr4UgWuU/HI5dhZmRvWZX05m8v6ORJ6eEsTt14l9UVNFOCEACwgICFxlBDEeAQEBgW6GEIAFBAQEugghAAsICAh0EUIAFhAQEOgihAAsICAg0EUIAVhAQECgixACsICAgEAX0aY+YJFIVABkXL3LERAQEOiR+Gk0GiO7mjYFYAEBAQGBjkNIQQgICAh0EUIAFhAQEOgihAAsICAg0EUIAVhAQECgixACsICAgEAXIQRgAQEBgS5CCMACAgICXYQQgAUEBAS6CCEACwgICHQR/w+z/VcIIg4o9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(X[:, 0], X[:, 1],s=6, cmap=cm, edgecolors='none')\n",
    "plt.title(u'原始数据分布图')\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.grid(True)\n",
    "plt.savefig('./pic01.png', dpi=600)#保存图片"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12709edf",
   "metadata": {},
   "source": [
    "# 子图2：K-Means算法聚类结果图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0d0599ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.scatter(X[:, 0], X[:, 1], c=km_y_hat, s=6, cmap=cm, edgecolors='none')\n",
    "# plt.scatter(k_means_cluster_centers[:, 0], k_means_cluster_centers[:, 1], c=range(clusters), s=60, cmap=cm2,\n",
    "#             edgecolors='none')\n",
    "plt.title(u'K-Means算法聚类结果图')\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "# plt.text(-2.8, 3, 'train time: %.2fms' % (km_batch * 1000))\n",
    "plt.grid(True)\n",
    "plt.savefig('kmeans_result.png', dpi=600)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa3426a1",
   "metadata": {},
   "source": [
    "# 子图三Mini Batch K-Means算法聚类结果图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d9a1e855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], c=mbkm_y_hat, s=6, cmap=cm, edgecolors='none')\n",
    "plt.title(u'Mini Batch K-Means算法聚类结果图')\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "# plt.text(-2.8, 3, 'train time: %.2fms' % (mbk_batch * 1000))\n",
    "plt.grid(True)\n",
    "plt.savefig('mini_result.png', dpi=600)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
